In [2]:
#!pip install mglearn
import mglearn
import sklearn
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import IPython

from sklearn.datasets import load_boston
boston = load_boston()
X, y = mglearn.datasets.load_extended_boston()

from sklearn.model_selection import train_test_split
X, y=mglearn.datasets.make_forge()

X_train, X_test, y_train, y_test=train_test_split(X, y, random_state=0)

from sklearn.neighbors import KNeighborsClassifier
clf=KNeighborsClassifier(n_neighbors=3), y_train)
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski')
clf.score(X_test, y_test)

from sklearn.linear_model import LinearRegression
lr=LinearRegression().fit(X_train, y_train)

print("training set score: %f" % lr.score(X_train, y_train))
print("test set score: %f" % lr.score(X_test, y_test))

from sklearn.linear_model import Ridge
ridge = Ridge().fit(X_train, y_train)
print("training set score: %f" % ridge.score(X_train, y_train))
print("test set score: %f" % ridge.score(X_test, y_test))

ridge01 = Ridge(alpha=0.1).fit(X_train, y_train)
print("training set score: %f" % ridge01.score(X_train, y_train))
print("test set score: %f" % ridge01.score(X_test, y_test))

print ("--------------------")

from sklearn.linear_model import Lasso
lasso00001 = Lasso(alpha=0.0001).fit(X_train, y_train)
print("training set score: %f" % lasso00001.score(X_train, y_train))
print("test set score: %f" % lasso00001.score(X_test, y_test))
print("number of features used: %d" % np.sum(lasso00001.coef_ != 0))

(506, 104)
training set score: 0.771865
test set score: 0.725968
training set score: 0.771686
test set score: 0.722415
training set score: 0.771863
test set score: 0.725626
training set score: 0.771865
test set score: 0.725916
number of features used: 2

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