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NEURAL NETWORK
Neural networks, also known as artificial neural networks (ANNs) or simulated
neural networks (SNNs), are a subset of machine learning and are at the heart
of deep learning algorithms. Their name and structure are inspired by the human
brain, mimicking the way that biological neurons signal to one another.
Neural networks rely on training data to learn and improve their accuracy over time.
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TENSORFLOW
‣ TensorFlow is a popular framework of machine learning and deep learning.
‣ The word TensorFlow is made by two words, i.e., Tensor and Flow
1. Tensor is a multidimensional array
2. Flow is used to define the flow of data in operation.
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ACTIVATION FUNCTION
It is used to determine the output of neural network like yes or no. It maps the resulting values in between 0 to 1 or -1 to 1 etc.
(depending upon the function).
The Activation Functions can be basically divided into 2 types-
‣ Linear Activation Function
‣ Non-linear Activation Functions
1. Linear or Identity Activation Function
This type of function represents a line or linear. Therefore, the output of the functions will not be confined between any range.
2. Non-linear Activation Function
The Nonlinear Activation Functions are the most used activation functions. Nonlinearity graphs are generally curves
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MULTI CLASS CLASSIFICATION
‣ Classification means categorising data and forming groups based on the similarities. In a
dataset, the independent variables or features play a vital role in classifying our data. When we
talk about multiclass classification, we have more than two classes in our dependent or target
variable,
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DECISION TREE
‣ Decision Tree is a Supervised learning technique that can be used for both classification and
Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured
classifier, where internal nodes represent the features of a dataset, branches represent the
decision rules and each leaf node represents the outcome.
‣ It is a graphical representation for getting all the possible solutions to a problem/decision based
on given conditions.
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ENSEMBLE METHODS BASED ON DECISION
TREES
‣ Random Forest (Regressor / Classifier)
‣ Extremely Randomized Trees (Regressor / Classifier)
‣ Bagging (Regressor / Classifier)
‣ Adaptive Booster (Regressor / Classifier)
‣ Gradient Boost (Regressor / Classifier)
‣ XGBoost (Regressor / Classifier)
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RANDOM FOREST
‣ 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
‣ The greater number of trees in the forest leads to higher accuracy and prevents the
problem of overfitting
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XGBOOST
XGBoost is a robust machine-learning algorithm that can help you understand your data and make better
decisions.
XGBoost is an implementation of gradient-boosting decision trees.
XgBoost is a gradient boosting algorithm for supervised learning. It's a highly efficient and scalable
implementation of the boosting algorithm, with performance comparable to that of other state-of-the-art
machine learning algorithms in most cases.
