在本文中，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以区分不同类型的水果。

本文的目的是确定最适合手头问题的机器学习算法; 因此，我们想要比较不同的算法，选择效果最好的算法。

数据

水果数据集由爱丁堡大学的Iain Murray博士创建。他买了几十个不同品种的桔子、柠檬和苹果，并记录了他们的尺寸。

让我们看一下数据的前几行。

%matplotlib inlineimport pandas as pdimport matplotlib.pyplot as pltfruits = pd.read_table('fruit_data_with_colors.txt')fruits.head()

数据集的每一行表示水果的一个部分，由列表中的几个特征表示。

我们的数据集中有59个水果和7个特征：print(fruits.shape)

(59,7)

我们的数据集中有四种类型的水果：

print(fruits['fruit_name'].unique())

['苹果''橘子(mandarin)''橙子''柠檬']

除橘子外，数据非常平衡。我们必须坚持下去。print(fruits.groupby('fruit_name').size())

import seaborn as snssns.countplot(fruits['fruit_name'],label='Count')plt.show()

可视化每个数字变量的方形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()

颜色分数近似于高斯分布。

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()

一些属性对是相关的(质量和宽度)。这表明高度相关性和可预测的关系。from pandas.tools.plotting import scatter_matrixfrom matplotlib import cmfeature_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')

统计摘要

我们可以看到数值没有相同的比例。我们需要对我们为训练集计算的测试集扩展应用。

创建训练和测试集扩展到应用。

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

Logistic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)))

Logistic回归分类器在训练集上的准确率:0.70

Logistic回归分类器在测试集上的准确率: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)))

决策树分类器在训练集上的准确率:1.00

决策树分类器在测试集上的准确率:0.73

K-Nearest Neighborsfrom 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)))

K-NN分类器在训练集上的准确率:0.95

K-NN分类器在测试集上的准确率: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)))

LDA分类器在训练集上的准确率:0.86

LDA分类器在测试集上的准确率: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)))

GNB分类器在训练集上的准确率:0.86

GNB分类器在测试集上的准确率: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)))

SVM分类器在训练集上的准确率:0.61

SVM分类器在测试集上的准确率:0.33

KNN算法是我们尝试过的最准确的模型。混淆矩阵表示测试集没有发生错误。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))

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')

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])

对于这个特定的数据集，当k = 5时，我们获得最高的准确度

总结

在本文中，我们关注预测的准确性。我们的目标是学习具有良好泛化性能的模型。这种模型使预测精度最大化。我们确定了最适合手头问题的机器学习算法(即水果类型分类); 因此，我们比较了不同的算法并选择了性能最佳的算法。