rcv = lm.RidgeCV(alphas=[0.001, 0.01, 0.1, 1, 10, 100, 1000], cv=5)
rcv.fit(XDF.values, y)
rcv
rcv.score(XDF.values, y)
get_ipython().set_next_input('lasso = lm.LassoCV')
get_ipython().run_line_magic('pinfo', 'lm.LassoCV')
lasso = lm.LassoCV(n_jobs=-1, cv=5)
lasso.fit(XDF.values, y)
lasso.score(XDF.values, y)
lasso.coef_
rcv.coef_
ecv = lm.ElasticNetCV()
ecv.fit(XDF.values, y)
ecv.score(XDF.values, y)
from sklearn.feature_selection import RFECV
RFECV.head()
RFECV
lr = lm.LinearRegression()
rfecv = RFECV()
rfecv = RFECV(lr, cv=5, n_jobs=-1)
rfecv.fit(XDF.values, y)
rfecv.grid_scores_
rfecv.grid_scores_.max()
XDF.var(0)
get_ipython().run_line_magic('whos', '')
df.head()
X.shape
X.head()
X.columns
XDF.columns
XDF.groupby('redirect')['n_count'].mean()
