CODEWITHSUNDEEP
machine-learningregressionscikit-learnlasso-regression
user644745
user644745

Reputation: 5713

Why do I get some negative values (predictors) as output of regressor estimators (Lasso, Ridge, ElasticNet)

For my regression problem, I am using GridSearchCV of scikit-learn to get the best alpha value and using this alpha value in my estimator (Lasso, Ridge, ElasticNet). My target values in the training dataset do not contain any negative values. But some of the predicted values are negative (around 5-10%). I am using the following code. My training data contains some Null values and I am replacing them by mean of that feature.

return Lasso(alpha=best_parameters['alpha']).fit(X,y).predict(X_test)

Any idea why am I getting some as Negative values ? Shape of X,y and X_test are (20L, 400L) (20L,) (10L, 400L)

Upvotes: 0

Views: 3360

Answers (1)

lejlot
lejlot

Reputation: 66805

Lasso is just regularized linear regression so in fact for each trained model there are some values for which the predictor will be negative.

consider a linar function 

f(x) = w'x + b

Where w and x are vectors and ' is transposition operator

No matter what are the values of w and b, as long as w is not a zero vector - there are always values of x for which f(x)<0. And it does not matter that your training set used to compute w and b did not contain any negative values, as the linear model will always (possibly in some really big values) cross the 0 value.

Upvotes: 3

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