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The intercept in regression-type models is the value of the Y variable when all X variables are 0.

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Meaning/interpretation of intercept_ in partial least squares

After using sklearn library for Partial Least Squares, I have doubts about the interpretation of the "intercept" of the model. … f'Intercept value: {plsReg.intercept_[0]:.4f}') p0 = pd.DataFrame(data = [[0,0,0,0]], columns = ['q','x', 'y', 'z']) uPredicted = plsReg.predict(p0) print(f'Predicted value: {uPredicted[0]:.4f}') Output Intercept
Francisco Angel's user avatar