Random Forest is a supervised learning ensemble algorithm. Ensemble algorithms are those which combine more than one algorithms of same or different kind for classifying objects....
What is an "ensemble learner"? How can we combine different base learners into an ensemble in order to improve the overall classification performance? In this lecture, we are providing some answers to these questions.
A brief presentation given on the basics of Ensemble Methods. Given as a 'Lightning Talk' during the 7th Cohort of General Assembly's Data Science Immersive Course
No machine learning algorithm dominates in every domain, but random forests are usually tough to beat by much. And they have some advantages compared to other models. No much input preparation needed, implicit feature selection, fast to train, and ability to visualize the model. While it is easy to get started with random forests, a good understanding of the model is key to get the most of them.
This talk will cover decision trees from theory, to their implementation in scikit-learn. An overview of ensemble methods and bagging will follow, to end up explaining and implementing random forests and see how they compare to other state-of-the-art models.
The talk will have a very practical approach, using examples and real cases to illustrate how to use both decision trees and random forests.
We will see how the simplicity of decision trees, is a key advantage compared to other methods. Unlike black-box methods, or methods tough to represent in multivariate cases, decision trees can easily be visualized, analyzed, and debugged, until we see that our model is behaving as expected. This exercise can increase our understanding of the data and the problem, while making our model perform in the best possible way.
Random Forests can randomize and ensemble decision trees to increase its predictive power, while keeping most of their properties.
The main topics covered will include:
* What are decision trees?
* How decision trees are trained?
* Understanding and debugging decision trees
* Ensemble methods
* Bagging
* Random Forests
* When decision trees and random forests should be used?
* Python implementation with scikit-learn
* Analysis of performance
What is an "ensemble learner"? How can we combine different base learners into an ensemble in order to improve the overall classification performance? In this lecture, we are providing some answers to these questions.
A brief presentation given on the basics of Ensemble Methods. Given as a 'Lightning Talk' during the 7th Cohort of General Assembly's Data Science Immersive Course
No machine learning algorithm dominates in every domain, but random forests are usually tough to beat by much. And they have some advantages compared to other models. No much input preparation needed, implicit feature selection, fast to train, and ability to visualize the model. While it is easy to get started with random forests, a good understanding of the model is key to get the most of them.
This talk will cover decision trees from theory, to their implementation in scikit-learn. An overview of ensemble methods and bagging will follow, to end up explaining and implementing random forests and see how they compare to other state-of-the-art models.
The talk will have a very practical approach, using examples and real cases to illustrate how to use both decision trees and random forests.
We will see how the simplicity of decision trees, is a key advantage compared to other methods. Unlike black-box methods, or methods tough to represent in multivariate cases, decision trees can easily be visualized, analyzed, and debugged, until we see that our model is behaving as expected. This exercise can increase our understanding of the data and the problem, while making our model perform in the best possible way.
Random Forests can randomize and ensemble decision trees to increase its predictive power, while keeping most of their properties.
The main topics covered will include:
* What are decision trees?
* How decision trees are trained?
* Understanding and debugging decision trees
* Ensemble methods
* Bagging
* Random Forests
* When decision trees and random forests should be used?
* Python implementation with scikit-learn
* Analysis of performance
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
Get to know in detail the termonologies of Random Forest with their types of algorithms used in the workflow along with their advantages and disadvantages of their predecessors.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Random Forest Algorithm - Random Forest Explained | Random Forest In Machine ...Simplilearn
This Random Forest Algorithm Presentation will explain how Random Forest algorithm works in Machine Learning. By the end of this video, you will be able to understand what is Machine Learning, what is classification problem, applications of Random Forest, why we need Random Forest, how it works with simple examples and how to implement Random Forest algorithm in Python.
Below are the topics covered in this Machine Learning Presentation:
1. What is Machine Learning?
2. Applications of Random Forest
3. What is Classification?
4. Why Random Forest?
5. Random Forest and Decision Tree
6. Comparing Random Forest and Regression
7. Use case - Iris Flower Analysis
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
CART: Not only Classification and Regression TreesMarc Garcia
Decision trees are very simple methods compared to Support Vector Machines, or Deep Learning. But they have some interesting properties that make them unique. For classification, for regression, or to extract probabilities, decision trees are easy to set up, and debug. And they are excellent to get a better understanding of your data.
This talk will cover Decision Trees, from theory, to their implementation in Python.
The talk will have a very practical approach, using examples and real cases to illustrate how to use decision trees, what we can expect from using them, and what kind of problems we will need to address.
The main topics covered will include:
* What are decision trees?
* How decision trees are trained?
* Data preprocessing for decision trees
* Understanding your data better with decision tree visualization
* Debugging decision trees using common sense and prior domain knowledge
* Avoiding overfitting, without cross-validation
* Python implementation
* Performance
Abstract: This PDSG workshop introduces basic concepts of ensemble methods in machine learning. Concepts covered are Condercet Jury Theorem, Weak Learners, Decision Stumps, Bagging and Majority Voting.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
This presentation educates you about Classification and
Regression trees (CART), CART decision tree methodology, Classification Trees, Regression Trees, Differences in CART, When to use CART?, Advantages of CART, Limitations of CART and What is a CART in Machine Learning?.
For more topics stay tuned with Learnbay.
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning.
Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
An ensemble is itself a supervised learning algorithm, because it can be trained and then used to make predictions. The trained ensemble, therefore, represents a single hypothesis. This hypothesis, however, is not necessarily contained within the hypothesis space of the models from which it is built.
Detailed talk about Random Forest and its statistical techniques for classification and regression analysis with termonologies like Out of Bag (OOB) estimate of performance, Bias Variance Trade off, and model validation metrics.
Let me know if anything is required. Happy to help, Talk soon! #bobrupakro
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
Get to know in detail the termonologies of Random Forest with their types of algorithms used in the workflow along with their advantages and disadvantages of their predecessors.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Random Forest Algorithm - Random Forest Explained | Random Forest In Machine ...Simplilearn
This Random Forest Algorithm Presentation will explain how Random Forest algorithm works in Machine Learning. By the end of this video, you will be able to understand what is Machine Learning, what is classification problem, applications of Random Forest, why we need Random Forest, how it works with simple examples and how to implement Random Forest algorithm in Python.
Below are the topics covered in this Machine Learning Presentation:
1. What is Machine Learning?
2. Applications of Random Forest
3. What is Classification?
4. Why Random Forest?
5. Random Forest and Decision Tree
6. Comparing Random Forest and Regression
7. Use case - Iris Flower Analysis
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
CART: Not only Classification and Regression TreesMarc Garcia
Decision trees are very simple methods compared to Support Vector Machines, or Deep Learning. But they have some interesting properties that make them unique. For classification, for regression, or to extract probabilities, decision trees are easy to set up, and debug. And they are excellent to get a better understanding of your data.
This talk will cover Decision Trees, from theory, to their implementation in Python.
The talk will have a very practical approach, using examples and real cases to illustrate how to use decision trees, what we can expect from using them, and what kind of problems we will need to address.
The main topics covered will include:
* What are decision trees?
* How decision trees are trained?
* Data preprocessing for decision trees
* Understanding your data better with decision tree visualization
* Debugging decision trees using common sense and prior domain knowledge
* Avoiding overfitting, without cross-validation
* Python implementation
* Performance
Abstract: This PDSG workshop introduces basic concepts of ensemble methods in machine learning. Concepts covered are Condercet Jury Theorem, Weak Learners, Decision Stumps, Bagging and Majority Voting.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
This presentation educates you about Classification and
Regression trees (CART), CART decision tree methodology, Classification Trees, Regression Trees, Differences in CART, When to use CART?, Advantages of CART, Limitations of CART and What is a CART in Machine Learning?.
For more topics stay tuned with Learnbay.
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning.
Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
An ensemble is itself a supervised learning algorithm, because it can be trained and then used to make predictions. The trained ensemble, therefore, represents a single hypothesis. This hypothesis, however, is not necessarily contained within the hypothesis space of the models from which it is built.
Detailed talk about Random Forest and its statistical techniques for classification and regression analysis with termonologies like Out of Bag (OOB) estimate of performance, Bias Variance Trade off, and model validation metrics.
Let me know if anything is required. Happy to help, Talk soon! #bobrupakro
Machine learning session6(decision trees random forrest)Abhimanyu Dwivedi
Concepts include decision tree with its examples. Measures used for splitting in decision tree like gini index, entropy, information gain, pros and cons, validation. Basics of random forests with its example and uses.
Random Forest Algorithm widespread popularity stems from its user-friendly nature and adaptability, enabling it to tackle both classification and regression problems effectively. The algorithm’s strength lies in its ability to handle complex datasets and mitigate overfitting, making it a valuable tool for various predictive tasks in machine learning.
One of the most important features of the Random Forest Algorithm is that it can handle the data set containing continuous variables, as in the case of regression, and categorical variables, as in the case of classification. It performs better for classification and regression tasks. In this tutorial, we will understand the working of random forest and implement random forest on a classification task.
Machine Learning Unit-5 Decesion Trees & Random Forest.pdfAdityaSoraut
Its all about Machine learning .Machine learning is a field of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit programming instructions. Instead, these algorithms learn from data, identifying patterns, and making decisions or predictions based on that data.
There are several types of machine learning approaches, including:
Supervised Learning: In this approach, the algorithm learns from labeled data, where each example is paired with a label or outcome. The algorithm aims to learn a mapping from inputs to outputs, such as classifying emails as spam or not spam.
Unsupervised Learning: Here, the algorithm learns from unlabeled data, seeking to find hidden patterns or structures within the data. Clustering algorithms, for instance, group similar data points together without any predefined labels.
Semi-Supervised Learning: This approach combines elements of supervised and unsupervised learning, typically by using a small amount of labeled data along with a large amount of unlabeled data to improve learning accuracy.
Reinforcement Learning: This paradigm involves an agent learning to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties, enabling it to learn the optimal behavior to maximize cumulative rewards over time.Machine learning algorithms can be applied to a wide range of tasks, including:
Classification: Assigning inputs to one of several categories. For example, classifying whether an email is spam or not.
Regression: Predicting a continuous value based on input features. For instance, predicting house prices based on features like square footage and location.
Clustering: Grouping similar data points together based on their characteristics.
Dimensionality Reduction: Reducing the number of input variables to simplify analysis and improve computational efficiency.
Recommendation Systems: Predicting user preferences and suggesting items or actions accordingly.
Natural Language Processing (NLP): Analyzing and generating human language text, enabling tasks like sentiment analysis, machine translation, and text summarization.
Machine learning has numerous applications across various domains, including healthcare, finance, marketing, cybersecurity, and more. It continues to be an area of active research and
Data Science - Part V - Decision Trees & Random Forests Derek Kane
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
An Introduction to Random Forest and linear regression algorithmsShouvic Banik0139
This presentation aims to provide a comprehensive understanding of the Random Forest and Linear Regression algorithms, their functioning, and significance. It is designed to equip the audience with the knowledge required to apply these algorithms effectively in practical scenarios, and to further enhance their expertise in the field.
This contains random forest algorithm in machine learning
Random Forest is a popular machine learning algorithm that belongs to the supervised learning technique. It can be used for both Classification and Regression problems in ML. It is based on the concept of ensemble learning, which is a process of combining multiple classifiers to solve a complex problem and to improve the performance of the model.
As the name suggests, "Random Forest is a classifier that contains a number of decision trees on various subsets of the given dataset and takes the average to improve the predictive accuracy of that dataset." Instead of relying on one decision tree, the random forest takes the prediction from each tree and based on the majority votes of predictions, and it predicts the final output.
The greater number of trees in the forest leads to higher accuracy and prevents the problem of overfitting.
Assumptions for Random Forest
Since the random forest combines multiple trees to predict the class of the dataset, it is possible that some decision trees may predict the correct output, while others may not. But together, all the trees predict the correct output. Therefore, below are two assumptions for a better Random forest classifier:
There should be some actual values in the feature variable of the dataset so that the classifier can predict accurate results rather than a guessed result.
The predictions from each tree must have very low correlations.
Why use Random Forest?
Below are some points that explain why we should use the Random Forest algorithm:
It takes less training time as compared to other algorithms.
It predicts output with high accuracy, even for the large dataset it runs efficiently.
It can also maintain accuracy when a large proportion of data is missing.
How does Random Forest algorithm work?
Random Forest works in two-phase first is to create the random forest by combining N decision tree, and second is to make predictions for each tree created in the first phase.
The Working process can be explained in the below steps and diagram:
Step-1: Select random K data points from the training set.
Step-2: Build the decision trees associated with the selected data points (Subsets).
Step-3: Choose the number N for decision trees that you want to build.
Step-4: Repeat Step 1 & 2.
Step-5: For new data points, find the predictions of each decision tree, and assign the new data points to the category that wins the majority votes.
The working of the algorithm can be better understood by the below example:
Example: Suppose there is a dataset that contains multiple fruit images. So, this dataset is given to the Random forest classifier. The dataset is divided into subsets and given to each decision tree. During the training phase, each decision tree produces a prediction result, and when a new data point occurs, then based on the majority of results, the Random Forest classifier predicts the final decision. Consider the below image:
A decision tree is a guide to the potential results of a progression of related choices. It permits an individual or association to gauge potential activities against each other dependent on their costs, probabilities, and advantages. They can be utilized either to drive casual conversation or to outline a calculation that predicts the most ideal decision scientifically.
machine learning (ML) is the area of computational science that focuses on analyzing and interpreting patterns and structures in data to enable learning, reasoning, and decision making outside of human interaction.
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2. Overview
Random Forest is a supervised learning ensemble algorithm. Ensemble algorithms are those which combine more than
one algorithms of same or different kind for classifying objects. The ‘forest’ that Random Forest Classifier builds, is an
ensemble of Decision Trees, most of the time trained with the ‘bagging’ method. The general idea of the bagging
method is that a combination of learning models increases the overall result.
Random forest classifier creates a set of decision trees from randomly selected subset of training set. It then aggregates
the votes from different decision trees to decide the final class of the test object.
Random Forest adds additional randomness to the model, while growing the trees. Instead of searching for the most
important feature while splitting a node, it searches for the best feature among a random subset of features. This
results in a wide diversity that generally results in a better model.
3. Explanation
Say, we have 1000 observations in the complete population with 10 variables. Random forest tries to build multiple
CART model with different sample and different initial variables. For instance, it will take a random sample of 100
observation and 5 randomly chosen initial variables to build a CART model. It will repeat the process (say) 10 times and
then make a final prediction on each observation. Final prediction is a function of each prediction. This final prediction
can simply be the mean of each prediction.
4. Each tree in a forest is grown as follows:
• If the number of cases in the training set is N, sample n cases at random (but with replacement) from the original
data. This sample will be the training set for growing the tree.
• If there are M input variables, a number m < M is specified such that at each node, m variables are selected at
random out of the M and the best split on these m is used to split the node. The value of m is held constant during
the forest growing.
• Each tree is grown to the largest extent possible. There is no pruning.
5. Forest Error rate depends on two things:
• The correlation between any two trees in the forest. Increasing the correlation increases the forest error rate.
• The strength of each individual tree in the forest. A tree with a low error rate is a strong classifier. Increasing the
strength of the individual trees decreases the forest error rate.
Reducing m reduces both the correlation and the strength. Increasing it increases both. Somewhere in between is an
"optimal" range of m (usually quite wide). Using the OOB error rate (explained in later slides) an optimal value of m can
quickly be found. This is the only adjustable parameter to which random forests is somewhat sensitive.
6. Features
• It is unexcelled in accuracy among current algorithms.
• It runs efficiently on large data bases.
• It can handle thousands of input variables without variable deletion.
• It gives estimates of what variables are important in the classification.
• It generates an internal unbiased estimate of the generalization error as the forest building progresses.
• It has an effective method for estimating missing data. It maintains accuracy even when a large proportion of the data are
missing.
• It has methods for balancing error in class population unbalanced data sets.
• Generated forests can be saved for future use on other data.
• Prototypes are computed that give information about the relation between the variables and the classification.
• The capabilities of the above can be extended to unlabeled data, leading to unsupervised clustering, data views and outlier
detection.
• It offers an experimental method for detecting variable interactions.
7. Out-Of-Bag (OOB)
When the training set for the current tree is drawn by sampling with replacement, about one-third of the
observations are left out of the sample.
This OOB (out-of-bag) data is used to get a running unbiased estimate of the classification error as trees are added to
the forest. It is also used to get estimates of variable importance.
Each tree is constructed using a different bootstrap sample from the original data. About one-third of the cases are left
out of the bootstrap sample and not used in the construction of the kth tree.
8. Out-Of-Bag (OOB) Error Estimate
Each tree is constructed using a different bootstrap sample from the original data. About one-third of the cases are
left out of the bootstrap sample and not used in the construction of the kth tree.
Put each case left out in the construction of the kth tree down the kth tree to get a classification. In this way, a test set
classification is obtained for each case in about one-third of the trees. At the end of the run, take j to be the class that
got most of the votes every time case n was OOB. The proportion of times that j is not equal to the true class of n
averaged over all cases is the OOB error estimate. This has proven to be unbiased in many tests.
10. Summary
Random Forest is a great algorithm to train early in the model development process, to see how it performs and it’s hard
to build a “bad” Random Forest, because of its simplicity. This algorithm is also a great choice, if you need to develop a
model in a short period of time. On top of that, it provides a pretty good indicator of the importance it assigns to your
features.
Random Forests are also very hard to beat in terms of performance. Of course you can probably always find a model that
can perform better, like a neural network, but these usually take much more time in the development. And on top of that,
they can handle a lot of different feature types, like binary, categorical and numerical.