python 分类

python 分类

在这篇机器学习入门教程中，我们将使用Python中最流行的机器学习工具scikit- learn,在Python中实现几种机器学习算法。使用简单的数据集来训练分类器区分不同类型的水果。

这篇文章的目的是识别出最适合当前问题的机器学习算法。因此，我们要比较不同的算法，选择性能最好的算法。让我们开始吧!

数据

水果数据集由爱丁堡大学的Iain Murray博士创建。他买了几十个不同种类的橘子、柠檬和苹果，并把它们的尺寸记录在一张桌子上。密歇根大学的教授们对水果数据进行了些微的格式化，可以从这里下载。

让我们先看一看数据的前几行。

%matplotlib inline

import pandas as pd

import matplotlib.pyplot as plt

fruits = pd.read_table( fruit_data_with_ )

fruits.head()

图1

数据集的每一行表示一个水果块，它由表中的几个特征表示。

在数据集中有59个水果和7个特征:

print(fruits.shape)

(59, 7)

在数据集中有四种水果:

print(fruits[ fruit_name ].unique())

[“苹果”柑橘”“橙子”“柠檬”]

除了柑橘，数据是相当平衡的。我们只好接着进行下一步。

print(fruits.groupby( fruit_name ).size())

图2

import seaborn as sns

(fruits[ fruit_name ],label=Count)

plt.show()

图

可视化

每个数字变量的箱线图将使我们更清楚地了解输入变量的分布:

fruits.drop( fruit_label , axis=1).plot(kind= box , subplots=True, layout=(2,2), sharex=False, sharey=False, figsize=(9,9),

title= Box Plot for each input variable )

plt.savefig( fruits_box )

plt.show()

图4

看起来颜分值近似于高斯分布。

import pylab as pl

fruits.drop( fruit_label ,axis=1).hist(bins=0, figsize=(9,9))

pl.suptitle(Histogram for each numeric input variable)

plt.savefig( fruits_hist )

plt.show()

图5

一些成对的属性是相关的(质量和宽度)。这表明了高度的相关性和可预测的关系。

from plotting import scatter_matrix

from matplotlib import cm

feature_names = [ mass , width , height , color_score ]

X = fruits[feature_names]

y = fruits[ fruit_label ]

cmap = cm.get_cmap( gnuplot )

scatter = pd.scatter_matrix(X, c = y, marker = o , s=40, hist_kwds={ bins :15}, figsize=(9,9), cmap = cmap)

plt.suptitle( Scatter-matrix for each input variable )

plt.savefig( fruits_scatter_matrix )

图6

统计摘要

图7

我们可以看到数值没有相同的缩放比例。我们需要将缩放比例扩展应用到我们为训练集计算的测试集上。

创建训练和测试集，并应用缩放比例

from _selection import train_test_split

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

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()

X_train = scaler.fit_transform(X_train)

X_test = (X_test)

构建模型

逻辑回归

from sklearn.linear_model import LogisticRegression

logreg = LogisticRegression()

logreg.fit(X_train, y_train)

print( Accuracy of Logistic regression classifier on training set: {:.2f}

.format(logreg.score(X_train, y_train)))

print( Accuracy of Logistic regression classifier on test set: {:.2f}

.format(logreg.score(X_test, y_test)))

训练集中逻辑回归分类器的精确度:0.70

测试集中逻辑回归分类器的精确度:0.40

决策树

from import DecisionTreeClassifier

clf = DecisionTreeClassifier().fit(X_train, y_train)

print( Accuracy of Decision Tree classifier on training set: {:.2f}

.format(clf.score(X_train, y_train)))

print( Accuracy of Decision Tree classifier on test set: {:.2f}

.format(clf.score(X_test, y_test)))

训练集中决策树分类器的精确度:1.00

测试集中决策树分类器的精确度:0.7

K-earest eighbors（K- ）

from import KeighborsClassifier

knn = KeighborsClassifier()

knn.fit(X_train, y_train)

print( Accuracy of K- classifier on training set: {:.2f}

.format(knn.score(X_train, y_train)))

print( Accuracy of K- classifier on test set: {:.2f}

.format(knn.score(X_test, y_test)))

训练集中K- 分类器的精确度:0.95

测试集中K- 分类器的精确度:1.00

线性判别分析

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

lda = LinearDiscriminantAnalysis()

lda.fit(X_train, y_train)

print( Accuracy of LDA classifier on training set: {:.2f}

.format(lda.score(X_train, y_train)))

print( Accuracy of LDA classifier on test set: {:.2f}

.format(lda.score(X_test, y_test)))

训练集中LDA分类器的精确度:0.86

测试集中LDA分类器的精确度:0.67

高斯朴素贝叶斯

from _bayes import GaussianB

gnb = GaussianB()

gnb.fit(X_train, y_train)

print( Accuracy of GB classifier on training set: {:.2f}

.format(gnb.score(X_train, y_train)))

print( Accuracy of GB classifier on test set: {:.2f}

.format(gnb.score(X_test, y_test)))

训练集中GB分类器的精确度:0.86

测试集中GB分类器的精确度:0.67

支持向量机

from sklearn.svm import SVC

svm = SVC()

svm.fit(X_train, y_train)

print( Accuracy of SVM classifier on training set: {:.2f}

.format(svm.score(X_train, y_train)))

print( Accuracy of SVM classifier on test set: {:.2f}

.format(svm.score(X_test, y_test)))

训练集中SVM分类器的精确度:0.61

测试集中SVM分类器的精确度:0.

K算法是我们尝试过的最精确的模型。混淆矩阵提供了在测试集上没有错误的指示。但是，测试集非常小。

from import classification_report

from import confusion_matrix

pred = knn.predict(X_test)

print(confusion_matrix(y_test, pred))

print(classification_report(y_test, pred))

图8

绘制k-分类器的决策边界

import as cm

from import ListedColormap, Boundaryorm

import matplotlib.patches as mpatches

import matplotlib.patches as mpatches

X = fruits[[ mass , width , height , color_score ]]

y = fruits[ fruit_label ]

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

def plot_fruit_knn(X, y, n_neighbors, weights):

X_mat = X[[ height , width ]].as_matrix()

y_mat = y.as_matrix()

# Create color maps

cmap_light = ListedColormap([ #FFAAAA , #AAFFAA , #AAAAFF , #AFAFAF ])

cmap_bold = ListedColormap([ #FF0000 , #00FF00 , #0000FF , #AFAFAF ])

clf = neighbors.KeighborsClassifier(n_neighbors, weights=weights)

clf.fit(X_mat, y_mat)

# Plot the decision boundary by assigning a color in the color map

# to each mesh point.

mesh_step_size = .01 # step size in the mesh

plot_symbol_size = 50

x_min, x_max = X_mat[:, 0].min() - 1, X_mat[:, 0].max()  1

y_min, y_max = X_mat[:, 1].min() - 1, X_mat[:, 1].max()  1

xx, yy = (np.arange(x_min, x_max, mesh_step_size),

np.arange(y_min, y_max, mesh_step_size))

Z = clf.predict(_[xx.ravel(), yy.ravel()])

# Put the result into a color plot

Z = Z.reshape(xx.shape)

plt.figure()

plt.pcolormesh(xx, yy, Z, cmap=cmap_light)

# Plot training points

plt.scatter(X_mat[:, 0], X_mat[:, 1], s=plot_symbol_size, c=y, cmap=cmap_bold, edgecolor = black )

plt.xlim((), ())

plt.ylim((), ())

patch0 = mpatches.Patch(color= #FF0000 , label= apple )

patch1 = mpatches.Patch(color= #00FF00 , label= mandarin )

patch2 = mpatches.Patch(color= #0000FF , label= orange )

patch = mpatches.Patch(color= #AFAFAF , label= lemon )

plt.legend(handles=[patch0, patch1, patch2, patch])

plt.xlabel( height (cm) )

plt.ylabel( width (cm) )

(4-Class classification (k = %i, weights = %s )

% (n_neighbors, weights))

plt.show()

plot_fruit_knn(X_train, y_train, 5, uniform )

图9

k_range = range(1, 20)

scores = []

for k in k_range:

knn = KeighborsClassifier(n_neighbors = k)

knn.fit(X_train, y_train)

scores.append(knn.score(X_test, y_test))

plt.figure()

plt.xlabel( k )

plt.ylabel( accuracy )

plt.scatter(k_range, scores)

plt.xticks([0,5,10,15,20])

图10

对于这个特定的数据集，当k = 5时，我们获得了最高精确度。

结语

在这篇文章中，我们关注的是预测的准确度。我们的目标是学习一个具有良好泛化性能的模型。这样的模型使预测准确度最大化。通过比较不同的算法，我们确定了最适合当前问题的机器学习算法(即水果类型分类)。

创建这个帖子的源代码可以在这里到。