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编辑：xiaoshan

在这篇文章中，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。

下载地址：https://github.com/susanli2016/Machine-Learning-with-Python/blob/master/fruit_data_with_colors.txt

让我们先看一看数据的前几行。

%matplotlib inlineimport pandas as pdimport matplotlib.pyplot as pltfruits = 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，它由表中的几个特性表示。

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 snssns.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 plfruits.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。

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_splitX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)from sklearn.preprocessing import MinMaxScalerscaler = 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 LogisticRegressionlogreg = 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 DecisionTreeClassifierclf = 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 KNeighborsClassifierknn = 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 LinearDiscriminantAnalysislda = 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 GaussianNBgnb = 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 SVCsvm = 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_reportfrom sklearn.metrics import confusion_matrixpred = 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 cmfrom matplotlib.colors import ListedColormap, BoundaryNormimport matplotlib.patches as mpatchesimport matplotlib.patches as mpatchesX = 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)。

源代码地址：https://github.com/susanli2016/Machine-Learning-with-Python/blob/master/Solving%20A%20Simple%20Classification%20Problem%20with%20Python.ipynb