python 分类_简单机器学习入门教程：用Python解决简单的水果分类问题

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_colors.txt')

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

sns.countplot(fruits['fruit_name'],label="Count")

plt.show()

图3

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

看起来颜色分值近似于高斯分布。

import pylab as pl

fruits.drop('fruit_label' ,axis=1).hist(bins=30, 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 pandas.tools.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 sklearn.model_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 = scaler.transform(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 sklearn.tree 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:0.73

K-Nearest Neighbors（K-NN ）

from sklearn.neighbors import KNeighborsClassifier

knn = KNeighborsClassifier()

knn.fit(X_train, y_train)

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

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

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

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

训练集中K-NN 分类器的精确度:0.95

测试集中K-NN 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 sklearn.naive_bayes import GaussianNB

gnb = GaussianNB()

gnb.fit(X_train, y_train)

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

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

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

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

训练集中GNB分类器的精确度:0.86

测试集中GNB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:0.33

KNN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 sklearn.metrics import classification_report

from sklearn.metrics 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-NN分类器的决策边界

import matplotlib.cm as cm

from matplotlib.colors import ListedColormap, BoundaryNorm

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.KNeighborsClassifier(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.meshgrid(np.arange(x_min, x_max, mesh_step_size),

np.arange(y_min, y_max, mesh_step_size))

Z = clf.predict(np.c_[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(xx.min(), xx.max())

plt.ylim(yy.min(), yy.max())

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

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

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

patch3 = mpatches.Patch(color='#AFAFAF', label='lemon')

plt.legend(handles=[patch0, patch1, patch2, patch3])

plt.xlabel('height (cm)')

plt.ylabel('width (cm)')

plt.title("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 = KNeighborsClassifier(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找到。