### What is Feature Scaling? Explain the different feature scaling techniques

Feature scaling is a data preprocessing technique that involves transforming the numerical values of features to a standardized scale.

Feature scaling is a data preprocessing technique that involves transforming the numerical values of features to a standardized scale.

In the context of machine learning, “noise” in a dataset refers to the presence of random variation in the data that does not reflect the underlying relationship between the input features and the target variable.

Logistic regression is a supervised learning algorithm that is used to predict the probability of categorical dependent variable.

Some of the popular use cases of the bag of words model are document similarity, text classification, feature generation, text clustering

Elastic net uses a weighted combination of the L1 and L2 penalties that are used in both LASSO and Ridge regression, respectively.

LASSO performs feature selection by shrinking the coefficients of variables to zero

If a primary interest is to conduct automatic variable selection, only LASSO can do that.

L2, or Ridge regularization, is a form of regularization in which the penalty is based on the squared magnitude of the coefficients.

L1 regularization, or LASSO (Least Absolute Shrinkage and Selection Operator), is a kind of regularization

Regularization involves adding a penalty for complexity to the model objective function to improve a modelâ€™s generalization performance.

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