In [1]:
#!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()
print(X.shape)
mglearn.plots.plot_knn_classification(n_neighbors=3)
plt.show()
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)
clf.fit(X_train, y_train)
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski')
clf.predict(X_test)
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))
In [ ]: