experiments.baselines.linear_regression.LinearRegressionBaseline¶
- class LinearRegressionBaseline¶
Bases:
LinearRegressionModel,SupervisedExperimentBaseline- __init__()¶
Implements a classifier that always predicts that the positive class has prob=0.5, regardless of input
- __repr__()¶
Return repr(self).
Methods
- fit(X, Y)¶
Train the model using the feature,label pairs
- Parameters:
X (NxM numpy ndarray) – features
Y (Nx1 numpy ndarray) – labels
- Returns:
weights from the fitted model
- Return type:
numpy ndarray
- predict(theta, X)¶
Predict label using the linear model
- Parameters:
theta (numpy ndarray) – The parameter weights
X (numpy ndarray) – The features
- Returns:
predicted labels
- Return type:
numpy ndarray
- train(X, y)¶
Train the model. Just a wrapper to parent’s fit() method.
- Parameters:
X (2D np.ndarray) – features
y (1D np.ndarray) – labels