An AI model is a mathematical framework that allows computers to learn from data without being explicitly programmed. Choosing the right AI model is important for harnessing the full potential of AI for a specific application. There are several categories of AI models, including supervised, unsupervised, semi-supervised, and reinforcement learning models. Key factors to consider when selecting a model include the problem type, model performance, explainability, complexity, data size and type, and validation strategies.

1. HOW TO CHOOSE THE RIGHT AI
MODEL FOR YOUR
APPLICATION?
Talk to our Consultant
 
Listen to the article
Arti몭cial intelligence (AI) has taken center stage in the tech world,
transforming industries and paving the way for a future of limitless
possibilities. But amidst all the buzz, you may 몭nd yourself wondering, “What
exactly is an AI model, and why is choosing the right one so crucial?”
 

2. An AI model is a mathematical framework that allows computers to learn
from data and make predictions or decisions without being explicitly
programmed to do so. They are the engines under the hood of AI, turning
raw data into meaningful insights and actions. These models range from the
LaMDA, designed by Google to converse on any topic, to GPT models,
created by OpenAI, that excel at generating human-like text. Each model has
its strengths and weaknesses, making them better suited to certain tasks
than others.
The sea of AI models available can be overwhelming, but understanding
these models and choosing the right one is key to harnessing the full
potential of AI for your speci몭c application. Making an informed choice could
mean the di몭erence between an AI system that e몭ciently solves your
problems or one that falls short of your objectives. It’s not just about jumping
on the AI bandwagon in this fast-paced AI-driven world. It’s about making
informed decisions and choosing the right tools for your unique
requirements. So, whether you are a retail business looking to streamline
operations, a healthcare provider aiming to improve patient outcomes, or an
educational institution striving to enrich learning experiences, picking the
right AI model is a critical 몭rst step in your AI journey.
This article will guide you through this complex landscape, equipping you
with the knowledge to make the best choice for your needs.
What is an AI model?
Understanding the di몭erent categories of AI models
Supervised learning
Unsupervised learning
Semi-supervised learning
Reinforcement learning
Deep learning
Importance of AI models
Selecting the right AI model/ML algorithm for your application

3. Linear regression
Deep Neural Networks (DNNs)
Logistic regression
Decision trees
Linear Discriminant Analysis (LDA)
Naïve Bayes
Support Vector Machines (SVMs)
Learning Vector Quantization (LVQ)
K-Nearest Neighbor (KNN)
Random forest
Factors to consider when choosing an AI model
Problem categorization
Model performance
Explanability of the model
Model complexity
Data set type and size
Feature dimensionality
Training duration and expense
Inference speed
Validation strategies used for AI model selection
What is an AI model?
Artificial Intelligence
(AI)
Machine Learning
(ML)
To incorporate human behavior and
Intelligence to machine or systems.
Methods to learn from data or past
experience, which automates
analytical model building.
Computation through multi­layer
neural networks and processing.
Deep
Learning
(DL)
LeewayHertz

4. An arti몭cial intelligence model is a sophisticated program designed to imitate
human thought processes – learning, problem-solving, decision-making, and
pattern recognition – by analyzing and processing data. You can think of it as
a digital brain; just as humans use their brains to learn from experience, an
AI model uses algorithms and tools to learn from data. This data could be
pictures, text, music, numbers, and you name it. The model ‘trains’ on this
data, hunting for patterns and connections. For instance, an AI model
designed to recognize faces will study thousands of images of faces, learning
to identify key features like eyes, nose, mouth, and ears.
Once the AI model has been trained, it can then make decisions or
predictions based on new data. Let us understand this with the face
recognition example: After its training, such a model could be used to unlock
a smartphone by recognizing the face of the user.
Moreover, AI models are incredibly versatile, utilized in various applications
such as natural language processing (helping computers understand human
language), image recognition (identifying objects in an image), predictive
analytics (forecasting future trends), and even in autonomous vehicles.
But how do we know if an AI model is doing its job well? Just as students are
tested on what they have learned, AI models are also evaluated. They are
given a new data set, di몭erent from what they were trained on. This ‘test’
data helps measure the e몭ectiveness of the AI model by checking metrics like
accuracy, precision, and recall. For example, a face-recognition AI model
would be tested to identify faces from a new set of images accurately.
Build robust AI apps with LeewayHertz!
Maximize your app’s e몭ciency with the right AI model.
Our expertise ensures accurate model selection, 몭ne-
tuning, and seamless integration.

5. Learn More
Understanding the different categories of
AI models
Arti몭cial Intelligence models can be broadly divided into a few categories,
each with its distinct characteristics and applications. Let us take a brief tour
of these models:
Supervised learning models: Imagine a teacher guiding a student through a
lesson; that’s essentially how supervised learning models work. Humans,
usually experts in a particular 몭eld, train these models by labeling data
points. For instance, they might label a set of images as “cats” or “dogs.”
This labeled data helps the model to learn so that it can make predictions
when presented with new, similar data. For example, after training, the
model should be able to correctly identify whether a new image is of a cat
or a dog. Such models are typically used for predictive analyses.
Unsupervised learning models: Unlike the supervised models,
unsupervised learning models are a bit like self-taught learners. They don’t
need humans to label the data for them. Instead, they are programmed to
identify patterns or trends in the input data independently. This enables
them to categorize the data or summarize the content without human
intervention. They are most often used for exploratory analyses or when
the data isn’t labeled.
Semi-supervised learning models: These models strike a balance between
supervised and unsupervised learning. Think of them as students who get
some initial guidance from a teacher but are left to explore and learn more
independently. In this approach, experts label a small subset of data, which
is used to train the model partially. The model then uses this initial learning
to label a larger dataset itself, a process known as “pseudo-labeling.” This
type of model can be used for both descriptive and predictive purposes.
Reinforcement learning models: Reinforcement learning models learn

6. much like a child does – through trial and error. These models interact with
their environment and learn to make decisions based on rewards and
penalties. The goal of reinforcement learning is to 몭nd the best possible
strategy, or ‘policy,’ to obtain the greatest reward over time. It’s commonly
used in areas like game playing and robotics.
Deep learning models: Deep learning models are a bit like the human
brain. The ‘deep’ in deep learning models refers to the presence of several
layers in their arti몭cial neural networks. These models are especially good
at learning from large, complex datasets and can automatically extract
features from the data, eliminating the need for manual feature extraction.
They have found immense success in tasks like image and speech
recognition.
Importance of AI models
Arti몭cial intelligence models have become integral to business operations in
the modern data-driven world. The sheer volume of data produced today is
massive and continually growing, posing a challenge to businesses
attempting to extract valuable insights from them. This is where AI models
step in as invaluable tools. They simplify complex processes, expedite tasks
that would otherwise take humans a substantial amount of time, and provide
precise outputs to enhance decision-making. Here are some of the signi몭cant
ways AI models contribute:
Data collection: In a competitive business environment where data is a key
di몭erentiator, gathering relevant data for training AI models is paramount.
Whether it is unexplored data sources or data domains where rivals have
restricted access, AI models help businesses tap into these resources
e몭ciently. Moreover, businesses can continually re몭ne their models by
regularly re-training using the latest data, enhancing their accuracy and
relevance.
Generation of new data: AI models, especially Generative Adversarial
Networks (GANs), can produce new data that resembles the training data.
They can create diverse outputs, ranging from artistic sketches to

7. They can create diverse outputs, ranging from artistic sketches to
photorealistic images, like the ones created by models such as DALL-E 2.
This opens up new avenues for innovation and creativity in various
industries.
Interpretation of large datasets: AI models excel at handling large datasets.
They can quickly analyze and extract meaningful patterns from vast,
complex data that would otherwise be impossible for humans to
comprehend. In a process known as model inference, AI models use input
data to predict corresponding outputs, even on unseen data or real-time
sensory data. This ability facilitates quicker, data-driven decision-making
within organizations.
Automation of tasks: Integrating AI models into business processes can
lead to signi몭cant automation. These models can handle di몭erent stages of
a work몭ow, including data input, processing, and 몭nal output presentation.
This results in e몭cient, consistent, and scalable processes, freeing up
human resources to focus on more complex and strategic tasks.
The above are some of the ways AI models are reshaping the business
landscape by harnessing the power of data. They play a vital role in giving
businesses a competitive edge by enabling e몭ective data collection, fostering
innovation through data generation, enabling an understanding of vast data
sets, and promoting process automation.
Selecting the right AI model/ML algorithm
for your application
Machine Learning
Supervised Learning
Classification Regression Clustering Decision Making
Unsupervised Learning Reinforcement Learning

8. K ­ Means
Clustering
Mean­shift
Clustering
DBSCAN
Clustering
Agglomerative
Hierarchical
Clustering
Gaussian Mixture
Linear Regression
Neural Network
Regression
Support Vector
Regression
Decision Tree
Regression
Lasso Regression
Ridge Regression
Naive Bayes
Classifier
Decision Trees
Support Vector
Machines
Random Forest
K ­ Nearest
Neighbors
Q­ Learning
R Learning
TD Learning
LeewayHertz
There are numerous AI models with distinct architectures and varying
degrees of complexity. Each model has its strengths and weaknesses based
on the algorithm it uses and is chosen based on the nature of the problem,
the data available, and the speci몭c task at hand. Some of the most frequently
utilized AI model algorithms are as follows:
Linear regression
Deep Neural Networks (DNNs)
Logistic regression
Decision trees
Linear Discriminant Analysis (LDA)
Naïve Bayes
Support Vector Machines (SVMs)
Learning Vector Quantization (LVQ)
K-Nearest Neighbor (KNN)
Random forest
Linear regression
Linear regression is a straightforward yet powerful machine-learning
algorithm. It operates assuming a linear relationship between the input and
output variables. This means that it predicts the output variable (dependent
variable) as a weighted sum of the input variables (independent variables)
plus a bias (also known as the intercept).
This algorithm is primarily used for regression problems, aiming to predict a

9. continuous output. For instance, predicting the price of a house based on its
attributes like size, location, age, proximity to amenities, and more is a typical
use case for linear regression. Each of these features is given a weight or
coe몭cient that signi몭es its importance or in몭uence on the 몭nal price.
Moreover, one of the critical strengths of linear regression is its
interpretability. The weights assigned to the features provide a clear
understanding of how each feature in몭uences the prediction, which can be
incredibly useful for understanding the problem at hand.
However, linear regression makes several assumptions about the data,
including linearity, independence of errors, homoscedasticity (equal variance
of errors), and normality. If these assumptions are violated, it may result in
less accurate or biased predictions.
Despite these limitations, Linear Regression remains a popular starting point
for many prediction tasks due to its simplicity, speed, and interpretability.
Deep Neural networks(DNNs)
Deep Neural Networks (DNNs) represent a class of arti몭cial
intelligence/machine learning models characterized by multiple ‘hidden’
layers existing between the input and output layers. Inspired by the intricate
neural network found within the human brain, DNNs are constructed using
interconnected units known as arti몭cial neurons.
Exploring how DNN models operate thoroughly is bene몭cial to truly
understanding these AI tools. They excel at discerning patterns and
relationships in data, attributing to their extensive application across
numerous 몭elds. Industries that leverage DNN models commonly deal with
tasks like speech recognition, image recognition, and Natural Language
Processing (NLP). These complex models have signi몭cantly contributed to
advancements in these areas, enhancing the machine’s understanding and
interpretation of human-like data.
Logistic regression

10. Logistic regression is a statistical model used for binary classi몭cation tasks,
meaning it is designed to handle two possible outcomes. Unlike linear
regression, which predicts continuous outcomes, logistic regression
calculates the probability of a certain class or event. It’s bene몭cial because it
provides both the signi몭cance and direction of predictors. Even though it’s
unable to capture complex relationships due to its linear nature, its
e몭ciency, ease of implementation, and interpretability make it an attractive
choice for binary classi몭cation problems. Additionally, logistic regression is
commonly used in 몭elds like healthcare for disease prediction, 몭nance for
credit scoring, or marketing for customer retention prediction. Despite its
simplicity, it forms a fundamental building block in the toolbox of machine
learning, providing valuable insights with lower computational costs,
especially when the relationships in the data are relatively straightforward
and not steeped in complexity.
Decision trees
Decision trees are an e몭ective supervised learning algorithm for classi몭cation
and regression tasks. They function by continuously subdividing the dataset
into smaller sections, simultaneously creating an associated decision tree.
The outcome is a tree with distinct decision nodes and leaf nodes. This
approach provides a clear if/then structure that is easy to understand and
implement. For example, if you opt to pack your lunch rather than purchase
it, you could save money. This simple yet e몭cient model traces back to the
early days of predictive analytics, showcasing its enduring usefulness in the
realm of AI.
Linear Discriminant Analysis (LDA)
LDA is a machine learning model that’s really good at 몭nding patterns and
making predictions, especially when we’re trying to distinguish between two
or more groups.
When we feed data to the LDA model, it works like a detective to 몭nd a
pattern or rule in the data. For example, if we’re trying to predict whether a

11. patient has a certain disease, the LDA model will analyze the patient’s
symptoms and try to 몭nd a pattern that can indicate whether the patient has
the disease or not.
Once the LDA model has found this rule, it can then use it to make
predictions about new data. So if we give the model the symptoms of a new
patient, it can use the rule it found to predict whether this new patient has
the disease or not.
LDA is also really good at simplifying complex data. Sometimes we have so
much data that it’s hard to make sense of it all. The LDA model can help by
reducing this data into a simpler form, making it easier for us to understand
without losing any important information.
Naïve Bayes
Naïve Bayes is a powerful AI model rooted in Bayesian statistics principles. It
applies Bayes’ theorem, assuming a strong (naïve) independence between
features. Given a data set, the model calculates the probability of each class
or outcome, considering each feature as independent. This makes Naïve
Bayes particularly e몭ective for large dimensional datasets commonly used in
spam 몭ltering, text classi몭cation, and sentiment analysis. Its simplicity and
e몭ciency are its key strengths. Building on its strengths, Naïve Bayes is quick
to build and run and easy to interpret, making it a good choice for
exploratory data analysis.
Moreover, due to its assumption of feature independence, it can handle
irrelevant features quite well, and its performance is relatively una몭ected by
them. Despite its simplicity, Naïve Bayes often outperforms more complex
models when the dimensionality of the data is high. Moreover, it requires
less training data and can update its model easily with new training data. Its
몭exible and adaptable nature makes it popular in numerous real-world
applications.

12. Build robust AI apps with LeewayHertz!
Maximize your app’s e몭ciency with the right AI model.
Our expertise ensures accurate model selection, 몭ne-
tuning, and seamless integration.
Learn More
Support Vector Machines (SVMs)
Support Vector Machines, or SVMs, are extensively utilized machine learning
algorithms for classi몭cation and regression tasks. These remarkable
algorithms excel by identifying an optimal hyperplane that most e몭ectively
divides data into distinct classes.
To delve deeper, imagine trying to distinguish two di몭erent data groups.
SVMs aim to discover a line (in 2D) or hyperplane (in higher dimensions) that
not only separates the groups but also stays as far away as possible from the
nearest data points of each group. These points are known as “support
vectors,” which are crucial in determining the optimal boundary.
SVMs are particularly well-suited to handle high-dimensional data and have
robust mechanisms to tackle over몭tting. Their ability to handle both linear
and non-linear classi몭cation using di몭erent types of kernels, such as linear,
polynomial, and Radial Basis Functions (RBF), adds to their versatility. SVMs
are commonly used in a variety of 몭elds, including text and image
classi몭cation, handwriting recognition, and biological sciences for protein or
cancer classi몭cation.
Learning Vector Quantization (LVQ)
Learning Vector Quantization (LVQ) is a type of arti몭cial neural network
algorithm that falls under the umbrella of supervised machine learning. This
technique classi몭es data by comparing them to prototypes representing
di몭erent classes, and it is particularly suitable for pattern recognition tasks.

13. LVQ operates by initially creating a set of prototypes from the training data,
which can be considered general representatives of each class in the dataset.
The algorithm then goes through each data point, measures its similarity to
each prototype, and assigns it to the class of the most similar prototype. The
key distinguishing feature of LVQ is its learning process. As the model
iterates through the data, it adjusts the prototypes to improve the
classi몭cation. This is achieved by bringing the prototype closer to the data
point if it belongs to the same class and pushing it away if it belongs to a
di몭erent class. LVQ is commonly used in scenarios where the data may not
be linearly separable or has complex decision boundaries. Some typical
applications include image recognition, text classi몭cation, and bioinformatics.
LVQ is particularly e몭ective when the dimensionality of the data is high, but
the amount of data is limited.
K-nearest neighbors
K-nearest neighbors, often abbreviated as KNN, is a powerful algorithm
typically employed for tasks like classi몭cation and Regression. Its mechanism
revolves around identifying the ‘k’ points in the training dataset closest to a
given test point.
This algorithm doesn’t create a generalized model for the entire dataset but
waits until the last moment before making any predictions. This is why KNN
is known as a lazy learning algorithm. KNN works by looking at the ‘k’ nearest
data points to the point in question in the dataset, where ‘k’ can be any
integer. Based on the values of these nearest neighbors, the algorithm then
makes its prediction. For example, the prediction might be the most common
class among the neighbors in classi몭cation.
One of the main advantages of KNN is its simplicity and ease of
interpretation. However, it can be computationally expensive, particularly
with large datasets, and may perform poorly when there are many irrelevant
features.

14. Random forest
Random forest is a powerful and versatile machine-learning method that falls
under the category of ensemble learning. Essentially, it’s a collection, or
“forest,” of decision trees, hence the name. Instead of relying on a single
decision tree, it leverages the power of multiple decision trees to generate
more accurate predictions.
The way it works is fairly straightforward. When a prediction is needed, the
random forest takes the input, runs it through each decision tree in the
forest, and makes a separate prediction. The 몭nal prediction is then
determined by averaging the predictions of each tree for regression tasks or
by majority vote for classi몭cation tasks.
This approach reduces the likelihood of over몭tting, a common problem with
single decision trees. Each tree in the forest is trained on a di몭erent subset of
the data, making the overall model more robust and less sensitive to noise.
Random forests are widely used and highly valued for their accuracy,
versatility, and ease of use.
Factors to consider when choosing an AI
model
When deciding on an AI model, these key factors need careful consideration:
Problem categorization
Problem categorization is an essential step in selecting an AI model. It
involves categorizing the problem based on the type of input and output. By
categorizing the problem, we can determine the suitable algorithms to apply.
If the data is labeled for input categorization, it falls under supervised
learning. If the data is unlabeled and we aim to uncover patterns or
structures, it belongs to unsupervised learning. On the other hand, if the goal
is to optimize an objective function through interactions with an
environment, it falls under reinforcement learning. If the model predicts
numerical values, output categorization is a regression problem. If the model

15. numerical values, output categorization is a regression problem. If the model
assigns data points to speci몭c classes, it is a classi몭cation problem. If the
model groups similar data points together without prede몭ned classes, it is a
clustering problem.
Once the problem is categorized, we can explore the available algorithms
suitable for the task. Implementing multiple algorithms in a machine learning
pipeline to compare their performance using carefully selected evaluation
criteria is recommended. The algorithm that yields the best results is chosen
automatically. Optionally, hyperparameters can be optimized using
techniques like cross-validation to 몭ne-tune the performance of each
algorithm. However, if time is limited, manually selected hyperparameters
can be su몭cient.
It is important to note that this explanation provides a general overview of
problem categorization and algorithm selection.
Model performance
When selecting an AI model, the foremost consideration should be the
model’s performance quality. You should lean towards algorithms that
amplify this performance. The nature of the problem often determines the
metrics that can be employed to analyze the model’s results. Frequently used
metrics encompass accuracy, precision, recall, and the f1-score. However, it
is important to note that not all metrics are universally applicable. For
instance, accuracy is not suitable when dealing with datasets that are not
evenly distributed. Hence, choosing an appropriate metric or set of metrics
to assess your model’s performance is an essential step before embarking on
the model selection process.
Explainability of the model
The ability to interpret and explain model outcomes is crucial in various
contexts. However, the issue is that numerous algorithms function like “black
boxes,” making explaining their results challenging, irrespective of how

16. excellent they may be. The inability to do so can become a signi몭cant
impediment in scenarios where explainability is critical. Certain models like
linear regression and decision trees fare better when it comes to
explainability. Hence, gauging the interpretability of each model’s results
becomes vital in choosing an appropriate model. Interestingly, complexity
and explainability often stand at opposite ends of the spectrum, leading us to
consider complexity as a crucial factor.
Model complexity
The complexity of a model plays a signi몭cant role in its capability to uncover
intricate patterns within the data, but this advantage can be o몭set by the
challenges it poses in maintenance and interpretability. There are a few key
notions to consider: Greater complexity can often lead to enhanced
performance, albeit at increased costs. There is an inverse relationship
between complexity and explainability; the more intricate the model is, the
tougher it is to elucidate its outputs. Apart from explainability, the cost
involved in constructing and maintaining a model is a pivotal factor in the
success of a project. The more complex a model is, the greater its impact will
be throughout the model’s lifecycle.
Data set type and size
The volume and kind of training data at your disposal are critical
considerations when selecting an AI model. Neural networks excel at
managing and interpreting large volumes of data, whereas a K-Nearest
Neighbors (KNN) model performs optimally with fewer examples. Beyond the
sheer quantity of data available, another consideration is how much data you
require to yield satisfactory results. Sometimes, a robust solution can be
developed with merely 100 training instances; you might need 100,000 at
other times. Understanding your problem and the required data volume
should guide your selection of a model capable of processing it. Di몭erent AI
models necessitate varying types and quantities of data for successful
training and operation. For instance, supervised learning models demand a

17. substantial amount of labeled data, which can be costly and labor-intensive
to acquire. Unsupervised learning models can function with unlabeled data,
but the results might lack meaningfulness if the data is noisy or irrelevant. In
contrast, reinforcement learning models require numerous interactions with
an environment in a trial-and-error fashion, which can be di몭cult to simulate
or model in reality.
Feature dimensionality
Dimensionality, in the context of selecting an AI model, is a signi몭cant aspect
to consider and can be perceived from two perspectives: a dataset’s vertical
and horizontal dimensions. The vertical dimension refers to the volume of
data available, while the horizontal dimension denotes the number of
features within the dataset. The role of the vertical dimension in choosing an
appropriate model has already been discussed. Meanwhile, the horizontal
dimension is equally vital to consider, as an increased number of features
often enhance the model’s ability to formulate superior solutions. However,
more features also add to the model’s complexity. The “Curse of
Dimensionality” phenomenon provides an insightful understanding of how
dimensionality in몭uences a model’s complexity. It’s noteworthy that not
every model is equally scalable with high-dimensional datasets. Therefore,
for managing high-dimensional datasets e몭ectively, it may be necessary to
incorporate speci몭c dimensionality reduction algorithms, such as Principal
Component Analysis (PCA), one of the most widely used methods for this
purpose.
Training duration and expense
The duration and cost of training a model are crucial considerations in
selecting an AI model. The question of whether to opt for a model that’s 98%
accurate but costs $100,000 to train or a slightly less accurate model at 97%
accuracy but only costs $10,000 is dependent on your speci몭c situation. AI
models that need to incorporate new data swiftly can’t a몭ord lengthy training
cycles. For instance, a recommendation system that requires regular updates

18. based on user interactions bene몭ts from a cost-e몭ective and swift training
cycle. Striking the right balance between the training time, cost, and model
performance is a key factor when architecting a scalable solution. It’s all
about maximizing e몭ciency without compromising the performance of the
model.
Inference speed
Inference speed, or the duration it takes a model to process data and
produce a prediction, is vital when choosing an AI model. Consider the
scenario of a self-driving vehicle system – it requires instantaneous decisions,
thereby ruling out models with slow inference times. To illustrate, the KNN
(K-Nearest Neighbors) model performs most of its computational work
during the inference stage, which can make it slower to generate predictions.
On the other hand, a decision tree model takes less time during the
inference phase, even though it may require a longer training period. So,
understanding the requirements of your use case in terms of inference
speed is crucial when deciding on an AI model.
Few additional points that you could consider including:
Real-time requirements: If the AI application needs to process data and
provide results in real-time, the model selection will need to consider this.
For instance, certain complex models may not be suitable due to their
longer inference times.
Hardware constraints: If the AI model needs to run on speci몭c hardware
(like a smartphone, an embedded system, or a speci몭c server
con몭guration), these constraints may in몭uence the type of model that can
be used.
Maintenance and updating: Models often need to be updated with new
data, and some models are easier to update than others. This could be an
important factor depending on the application.
Data privacy: If the AI application deals with sensitive data, you may want
to consider models that can learn without needing access to raw data. For
instance, federated learning is a machine learning approach that allows for

19. instance, federated learning is a machine learning approach that allows for
model training on decentralized devices or servers holding local data
samples without exchanging them.
Model robustness and generalization: The model’s ability to generalize
from its training data to unseen data, and its robustness to adversarial
attacks or shifts in the data distribution, are important considerations.
Ethical considerations and bias: AI models can unintentionally introduce or
amplify bias, leading to unfair or unethical outcomes. Strategies for
understanding and mitigating these biases should be included.
Speci몭c AI model architectures: You may want to delve deeper into speci몭c
AI architectures such as Convolutional Neural Networks (CNNs) for image
processing tasks, Recurrent Neural Networks (RNNs) or Transformer-based
models for sequential data, and other architectures suited to speci몭c tasks
or data types.
Ensemble methods: These methods combine several models to achieve
better performance than any single model.
AutoML and Neural Architecture Search (NAS): These techniques
automatically search for the best model architecture and
hyperparameters, which can be very helpful when the best model type is
unknown or too costly to 몭nd manually.
Transfer learning: Using pre-trained models for similar tasks to leverage
the knowledge gained from previous tasks is a common practice in AI. This
reduces the requirement for large data and saves computational time.
Remember, the best AI model depends on your needs, resources, and
constraints. It’s all about 몭nding the right balance to meet your project’s
objectives.
Build robust AI apps with LeewayHertz!
Maximize your app’s e몭ciency with the right AI model.
Our expertise ensures accurate model selection, 몭ne-
tuning, and seamless integration.

20. tuning, and seamless integration.
Learn More
Validation strategies used for AI model
selection
Validation techniques are key in model selection as they help assess a
model’s performance on unseen data, ensuring that the selected model can
generalize well.
Resampling methods: Resampling methods in AI are techniques used to
assess and evaluate the performance of machine learning models on
unseen data samples. These methods involve rearranging and reusing the
available data to gain insights into how well the model can generalize.
Examples of resampling methods include random split, time-based split, K-
fold cross-validation, bootstrap, and strati몭ed K-fold. Resampling methods
help mitigate biases in data sampling, ensure accurate model evaluation,
handle time series data, stabilize model performance, and address issues
like over몭tting. They play a crucial role in model selection and performance
assessment in AI applications.
Random split: Random split is a technique that randomly distributes a
portion of data into training, testing, and, ideally, validation subsets. The
main advantage of this method is that it provides a good likelihood that all
three subsets will represent the original population fairly well. Put simply,
random splitting helps avoid biased data sampling. The use of the
validation set in model selection is worth noting. This second test set
prompts the question, why do we need two test sets? During the feature
selection and model tuning phases, the test set is used for evaluating the
model. This implies that the model’s parameters and feature set are
chosen to deliver an optimal result on the test set. Hence, the validation
set, which comprises completely unseen data points (that haven’t been
utilized in the tuning and feature selection stages), is used to evaluate the

21. utilized in the tuning and feature selection stages), is used to evaluate the
model.
Time-based split: In certain scenarios, data cannot be split randomly. For
example, when training a model for weather forecasting, the data cannot
be split randomly into training and testing sets, as this would disrupt the
seasonal patterns. This type of data is often termed as Time Series data. In
such situations, a time-oriented split is employed. For instance, the training
set could contain data from the past three years and the initial ten months
of the current year. The remaining two months of data could be designated
as the testing or validation set. There’s also a concept known as ‘window
sets’ – the model is trained up until a speci몭c date and then tested on
subsequent dates in an iterative manner such that the training window
progressively shifts forward by one day (correspondingly, the test set
decreases by a day). The advantage of this method is that it helps stabilize
the model and prevent over몭tting, especially when the test set is relatively
small (e.g., 3 to 7 days). However, time-series data does have a downside in
that the events or data points are not mutually independent. A single event
might in몭uence all subsequent data inputs. For instance, changing the
ruling political party might signi몭cantly alter population statistics in the
following years. Or a global event like the COVID-19 pandemic can
profoundly impact economic data for the forthcoming years. In such
scenarios, a machine learning model cannot learn e몭ectively from past
data because signi몭cant disparities exist between data points before and
after the event.
K-fold cross-validation: K-fold cross-validation is a robust validation
technique that begins with randomly shu몭ing the dataset and
subsequently dividing it into k subsets. The procedure then iteratively
treats each subset as a test set while all other subsets are pooled together
to form the training set. The model is trained on this training set and
subsequently tested on the test set. This process repeats k times, once for
each subset, resulting in k di몭erent outcomes from the k di몭erent test
sets. This comprehensively evaluates the model’s performance across

22. di몭erent parts of the data. By the end of this iterative process, the best-
performing model can be identi몭ed and chosen based on the highest
average score it achieves across the k-test sets. This method provides a
more balanced and thorough assessment of a model’s performance than a
single train/test split.
Strati몭ed K-fold: Strati몭ed K-fold is a variation of K-fold cross-validation
that considers the distribution of the target variable. This is a key
di몭erence from regular K-fold cross-validation, which does not consider
the target variable’s values during the splitting process. In strati몭ed K-fold,
the procedure ensures that each data fold contains a representative ratio
of the target variable classes. For example, if you have a binary
classi몭cation problem, strati몭ed K-fold will distribute the instances of both
classes evenly across all folds. This approach enhances the accuracy of
model evaluation and reduces bias in model training, ensuring that each
fold is a good representative of the overall dataset. Consequently, the
model’s performance metrics are likely more reliable and indicate its
performance on unseen data.
Bootstrap: Bootstrap is a powerful method used to achieve a stable model,
and it shares similarities with the random splitting technique due to its
reliance on random sampling. To begin bootstrapping, you 몭rst select a
sample size, which is generally the same size as your original dataset.
Following this, you randomly choose a data point from the original dataset
and include it in the bootstrap sample. Importantly, after selecting a data
point, you replace it back into the original dataset. This “sampling with
replacement” process is repeated N times, where N is the sample size. As a
result of this resampling technique, your bootstrap sample might contain
multiple occurrences of the same data point because every selection is
made from the full set of original data points, regardless of previous
selections. The model is then trained using this bootstrap sample. For
evaluation, we use the data points that were not selected in the bootstrap
process, known as out-of-bag samples. This technique e몭ectively measures
how well the model is likely to perform on unseen data.

23. how well the model is likely to perform on unseen data.
Probabilistic measures: Probabilistic measures provide a comprehensive
evaluation of a model by considering the model’s performance and
complexity. The complexity of a model is indicative of its ability to capture
the variability in the data. For instance, a model like linear regression,
prone to high bias, is considered less complex, while a neural network,
capable of modeling complex relationships, is viewed as highly complex.
When considering probabilistic measures, it’s essential to mention that the
model’s performance is calculated solely based on the training data,
eliminating the need for a separate test set. However, a limitation of
probabilistic measures is their disregard for the uncertainty inherent in
models. This could lead to a propensity for selecting simpler models at the
expense of more complex ones, which might not always be the optimal
choice.
Akaike Information Criterion (AIC): Akaike Information Criterion (AIC),
developed by statistician Hirotugu Akaike, is used to evaluate the quality of
a statistical model. The main premise of AIC is that no model can
completely capture the true reality, and thus there will always be some
information loss. This information loss can be quanti몭ed using the
Kullback-Leibler (KL) divergence, which measures the divergence between
the probability distributions of two variables. Akaike identi몭ed a correlation
between KL divergence and Maximum Likelihood, a principle where the
goal is to maximize the conditional probability of observing a data point X,
given certain parameters and a speci몭ed probability distribution. As a
result, Akaike formulated the Information Criterion (IC), now known as AIC,
which quanti몭es information loss, gauging the discrepancy between
di몭erent models. The preferred model is the one that has the minimum
information loss. However, AIC has its limitations. While it is excellent for
identifying models that minimize training information loss, it may struggle
with generalization. It tends to favor complex models, which may perform
well on training data but may not perform as well on unseen data.
Bayesian Information Criterion (BIC): Bayesian Information Criterion (BIC),

24. grounded in Bayesian probability theory, is designed to evaluate models
trained using maximum likelihood estimation. The fundamental aspect of
BIC is its incorporation of a penalty term for the complexity of the model.
The principle behind this is simple: the more complex a model, the higher
the risk of over몭tting, especially with small datasets. So, BIC attempts to
counteract this by adding a penalty that increases with the complexity of
the model. This ensures the model does not become overly complicated,
avoiding over몭tting. However, a key consideration when using BIC is the
size of the dataset. It’s more suitable when the dataset is large because,
with smaller datasets, BIC’s penalization could result in the selection of
oversimpli몭ed models that may not capture all the nuances in the data.
Minimum Description Length (MDL): The Minimum Description Length
(MDL) is a principle that originates from information theory, a 몭eld that
deals with measures like entropy to quantify the average bit
representation required for an event given a particular probability
distribution or random variable. The MDL refers to the smallest number of
bits necessary to represent a model. The goal under the MDL principle is to
몭nd a model that can be described using the fewest bits. This means
몭nding the most compact representation of the model, keeping the
complexity under control while accurately representing the data. This
approach fosters simplicity and parsimony in model representation, which
can help avoid over몭tting and improve model generalizability.
Structural Risk Minimization (SRM): Structural Risk Minimization (SRM)
addresses the challenge machine learning models face in 몭nding a general
theory from limited data, which often leads to over몭tting. Over몭tting
occurs when a model becomes excessively tailored to the training data,
compromising its ability to generalize to unseen data. SRM aims to strike a
balance between the model’s complexity and its ability to 몭t the data. It
recognizes that a complex model may 몭t the training data very well but can
su몭er from poor performance on new, unseen data. On the other hand, a
simple model may not capture the intricacies of the data and may under몭t.
By incorporating the concept of structural risk, SRM seeks to minimize both

25. By incorporating the concept of structural risk, SRM seeks to minimize both
the training error and the model complexity. It avoids excessive complexity
that could lead to over몭tting while still achieving reasonable accuracy on
unseen data. SRM promotes selecting a model that achieves a good trade-
o몭 between model complexity and data 몭tting, thus improving
generalization capabilities.
Each validation technique has its unique strengths and limitations, which
must be carefully considered in the model selection process.
Endnote
In the realm of arti몭cial intelligence, choosing the right AI model is a blend of
science, engineering, and a dash of intuition. It’s about understanding the
intricacies of supervised, unsupervised, semi-supervised, and reinforcement
learning. It’s about delving deep into the world of deep learning, appreciating
the strengths of di몭erent model types – from the elegance of linear
regression, the complexities of deep neural networks and the versatility of
decision trees to the probabilistic magic of Naive Bayes and the collaborative
power of random forests.
However, choosing the right AI model isn’t just a matter of understanding
these models. It’s about factoring in the problem at hand, weighing the pros
and cons of model complexity against performance, comprehending your
data type and size, and accommodating the dimensions of your features. It’s
about considering the resources, time, and expense needed for training and
the speed required for inference. It’s about ensuring a thorough validation
strategy for your model selection.
Choosing the right AI model is a journey, a labyrinth of choices and
considerations. But with the knowledge of AI models and the considerations
involved, you are ready to make your way through this complex process, one
decision at a time. So take this knowledge, apply it to your unique
application, and unlock the true potential of arti몭cial intelligence. Ultimately,
it’s not just about choosing an AI model but the right path to a more

26. it’s not just about choosing an AI model but the right path to a more
intelligent future.
Ready to leverage the power of arti몭cial intelligence but lost in a sea of choices?
Contact LeewayHertz’s AI consulting team and let our experts handpick the
perfect AI model for your application. Dive into the future with us!
Author’s Bio
Akash Takyar
CEO LeewayHertz
Akash Takyar is the founder and CEO at LeewayHertz. The experience of
building over 100+ platforms for startups and enterprises allows Akash to
rapidly architect and design solutions that are scalable and beautiful.
Akash's ability to build enterprise-grade technology solutions has attracted
over 30 Fortune 500 companies, including Siemens, 3M, P&G and Hershey’s.
Akash is an early adopter of new technology, a passionate technology
enthusiast, and an investor in AI and IoT startups.
Write to Akash

27. Start a conversation by filling the form
Once you let us know your requirement, our technical expert will schedule a
call and discuss your idea in detail post sign of an NDA.
All information will be kept con몭dential.
Name Phone
Company Email
Tell us about your project
Send me the signed Non-Disclosure Agreement (NDA )
Start a conversation
Insights

28. AI in procurement: Redefining efficiency through
automation
Arti몭cial intelligence is playing a transformative role in procurement,
bringing e몭ciency and optimization to decision-making and operational
processes.
From data to direction: How AI in sentiment
analysis redefines decision­making for businesses
Read More

29. LEEWAYHERTZPORTFOLIO
AI for sentiment analysis is an innovative way to automatically decipher the
emotional tone embedded in comments, giving businesses quick, real-time
insights from vast sets of customer data.
How is generative AI disrupting the insurance
sector?
Generative AI disrupts the insurance sector with its transformative
capabilities, streamlining operations, personalizing policies, and rede몭ning
customer experiences.
Read More
Read More
Show all Insights

30. SERVICES GENERATIVE AI
INDUSTRIES PRODUCTS
CONTACT US
Get In Touch
415-301-2880
info@leewayhertz.com
jobs@leewayhertz.com
388 Market Street
Suite 1300
San Francisco, California 94111
About Us
Global AI Club
Careers
Case Studies
Work
Community
TraceRx
ESPN
Filecoin
Lottery of People
World Poker Tour
Chrysallis.AI
Generative AI
Arti몭cial Intelligence & ML
Web3
Blockchain
Software Development
Hire Developers
Generative AI Development
Generative AI Consulting
Generative AI Integration
LLM Development
Prompt Engineering
ChatGPT Developers
Consumer Electronics
Financial Markets
Healthcare
Logistics
Manufacturing
Startup
Whitelabel Crypto Wallet
Whitelabel Blockchain Explorer
Whitelabel Crypto Exchange
Whitelabel Enterprise Crypto Wallet
Whitelabel DAO
 

31. Privacy & Cookies Policy
Sitemap
©2023 LeewayHertz. All Rights Reserved.