SequentialFeatureSelector is a feature selection technique. It is part of the feature_selection module and is used for selecting a subset of features from the original feature set. This technique follows a forward or backward sequential selection strategy. Here’s a brief overview: SequentialFeatureSelector is often used in conjunction with machine learning models to identify the most…
One-hot encoding is a technique used in machine learning and data preprocessing to represent categorical variables as binary vectors. In one-hot encoding, each category or label in a categorical variable is represented as a binary vector, where each element corresponds to a unique category. The process involves the following steps: For example, consider a dataset…
Model coefficients, also known as regression coefficients or weights, are the values assigned to the features (independent variables) in a regression model. In a linear regression model, the relationship between the input features (X) and the predicted output (y) is represented as: Here: The model coefficients are estimated during the training of the regression model.…
PolynomialFeatures is a preprocessing technique used in machine learning, particularly in polynomial regression. It transforms an input feature matrix by adding new features that are polynomial combinations of the original features. For example, if you have a feature (x), PolynomialFeatures can generate additional features like , etc., up to a specified degree. This allows the…
