choose-models 0.1.5

Model Selection Tool

📦 Models Class – Regression Models Playground

This class helps you quickly test different regression algorithms (OLS, SGD, BGD) on any DataFrame and target.

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✂️ split_data(ratio=0.8, randomState=42)

Splits data into train/test based on ratio. Uses self.target_column to separate X and y.

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📈 Linear_Regression_OLS(get_equation=False, plot=False, accuracy=True)

Trains simple (1D) Linear Regression using Ordinary Least Squares.

• get_equation: print learned line
• plot: visualize
• accuracy: print score

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📊 MLinear_Regression_OLS(get_equation=False, plot=False, accuracy=True)

Multi-feature version of OLS Linear Regression.

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⚡ MLinear_Regression_SGD(epochs=100, learning_rate=0.01, plot=False, accuracy=True, get_equation=False)

Multi-feature SGD Linear Regression. Trains with Stochastic Gradient Descent.

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🌀 Linear_Regression_BGD(epochs=100, learning_rate=0.01, plot=False, accuracy=True, get_equation=False)

Simple (1D) Linear Regression using Batch Gradient Descent.

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💪 MLinear_Regression_BGD(epochs=100, learning_rate=0.01, plot=False, accuracy=True, get_equation=False)

Multi-feature BGD-based Linear Regression.

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🧼 Standard_Scale(features=None)

Standardizes features (z-score normalization). Applies to entire DataFrame if no features are specified. Re-splits data after scaling.

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🚀 Linear_Regression_SGD(epochs=100, learning_rate=0.01, plot=False, accuracy=True, get_equation=False)

Simple (1D) Linear Regression using SGD.

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📤 Extract_Data()

Returns (X_train, X_test, y_train, y_test) — useful for external use.

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🆕 Set_DF(newDF, target_column)

Reset the class with a new DataFrame and target column.

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