Answers for "lasso regression scikit"

scikit learn lasso regression

from sklearn import linear_model
reg = linear_model.Lasso(alpha=0.1).fit(X, y)
reg.fit(X, y) #We can fit Lasso to the dataset in this way too
clf.score(X, y) #Return the mean accuracy on the given test data and labels
cfl.predict(X) #Return the predictions

#Regression Metrics
#Mean Absolute Error

from sklearn.metrics import mean_absolute_error 
mean_absolute_error(y_true, y_pred)

#Mean Squared Error

from sklearn.metrics import mean_squared_error
mean_squared_error(y_true, p_pred)

#R2 Score

from sklearn.metrics import r2_score
r2_score(y_true, y_pred)

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lasso regression implementation python

from sklearn.linear_model import Lasso
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()

X_train, X_test, y_train, y_test = train_test_split(X_data, y_data,
                                                   random_state = 0)

X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

linlasso = Lasso(alpha=2.0, max_iter = 10000).fit(X_train_scaled, y_train)