解释mean_scores = df.mean() plt.bar(mean_scores.index, mean_scores.values) plt.xlabel('科目') plt.ylabel('平均分') plt.show()

这段代码的作用是：首先计算了一个DataFrame（df）中每个列的平均值，然后将这些平均值作为纵坐标，在横坐标上显示各个科目的名称，并用条形图的形式将其可视化。

具体来说，代码中的 `mean_scores = df.mean()` 计算了df中每列的平均值，并将结果存储在mean_scores这个Series对象中。接着，`plt.bar(mean_scores.index, mean_scores.values)` 用`bar()`函数创建了条形图，其中横坐标是科目名称，纵坐标是平均分，这些数据分别从mean_scores对象的索引和值中获取。`plt.xlabel()` 和 `plt.ylabel()` 设置了横纵坐标的标签。最后，`plt.show()` 显示了可视化结果。

相关问题

rf = RandomForestClassifier(n_estimators=90, max_depth=) train_sizes, train_scores, cv_scores = learning_curve(rf,x_train,y_train,cv=5,train_sizes=np.linspace(0.01,1,100)) train_scores_mean = np.mean(train_scores, axis=1) train_scores_std = np.std(train_scores, axis=1) cv_scores_mean = np.mean(cv_scores, axis=1) cv_scores_std = np.std(cv_scores, axis=1) fig = plt.figure(figsize=(8,6), dpi=200) ax = fig.add_axes([0.1, 0.1, 0.8, 0.8]) ax.plot(train_sizes, train_scores_mean, color='dodgerblue', alpha=0.8) ax.plot(train_sizes, cv_scores_mean, color='g', alpha=0.8) ax.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color="dodgerblue") ax.fill_between(train_sizes, cv_scores_mean - cv_scores_std, cv_scores_mean + cv_scores_std, alpha=0.1, color="g") ax.legend(labels=['train_set_scores', 'cross_val_scores'], loc='best') ax.set_title('Learning curve of the random forests') ax.grid(True) ax.set_xlabel('The number of training samples') ax.set_ylabel('Model score') plt.savefig('Learning curve of the random forests.jpg') plt.show()

这段代码使用了随机森林分类器（RandomForestClassifier）进行训练，并使用学习曲线（learning_curve）来评估模型的性能。具体来说，它通过在训练集上逐渐增加样本数量，并在交叉验证集上计算模型得分，来绘制训练集和交叉验证集得分随训练集样本数量变化的曲线。这可以帮助我们判断模型是否存在过拟合或欠拟合的问题。

其中，n_estimators是指随机森林中树的个数，max_depth是指每棵树的最大深度。learning_curve函数中的train_sizes参数指定了训练集的大小范围，np.linspace(0.01,1,100)表示从0.01到1均匀取100个数，即训练集大小从1%到100%。cv参数指定了交叉验证的折数，这里设置为5。最后，绘制了训练集得分和交叉验证集得分随训练集样本数量变化的曲线，并保存了图片。

import seaborn as sns corrmat = df.corr() top_corr_features = corrmat.index plt.figure(figsize=(16,16)) #plot heat map g=sns.heatmap(df[top_corr_features].corr(),annot=True,cmap="RdYlGn") plt.show() sns.set_style('whitegrid') sns.countplot(x='target',data=df,palette='RdBu_r') plt.show() dataset = pd.get_dummies(df, columns = ['sex', 'cp', 'fbs','restecg', 'exang', 'slope', 'ca', 'thal']) from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler standardScaler = StandardScaler() columns_to_scale = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak'] dataset[columns_to_scale] = standardScaler.fit_transform(dataset[columns_to_scale]) dataset.head() y = dataset['target'] X = dataset.drop(['target'], axis=1) from sklearn.model_selection import cross_val_score knn_scores = [] for k in range(1, 21): knn_classifier = KNeighborsClassifier(n_neighbors=k) score = cross_val_score(knn_classifier, X, y, cv=10) knn_scores.append(score.mean()) plt.plot([k for k in range(1, 21)], knn_scores, color='red') for i in range(1, 21): plt.text(i, knn_scores[i - 1], (i, knn_scores[i - 1])) plt.xticks([i for i in range(1, 21)]) plt.xlabel('Number of Neighbors (K)') plt.ylabel('Scores') plt.title('K Neighbors Classifier scores for different K values') plt.show() knn_classifier = KNeighborsClassifier(n_neighbors = 12) score=cross_val_score(knn_classifier,X,y,cv=10) score.mean() from sklearn.ensemble import RandomForestClassifier randomforest_classifier= RandomForestClassifier(n_estimators=10) score=cross_val_score(randomforest_classifier,X,y,cv=10) score.mean()的roc曲线的代码

以下是绘制ROC曲线的代码：

from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import StratifiedKFold

cv = StratifiedKFold(n_splits=10)

# KNN Classifier ROC Curve
knn_classifier = KNeighborsClassifier(n_neighbors=12)
knn_tprs = []
knn_aucs = []
mean_fpr = np.linspace(0, 1, 100)

fig, ax = plt.subplots()
for i, (train, test) in enumerate(cv.split(X, y)):
    knn_classifier.fit(X.iloc[train], y.iloc[train])
    knn_proba = knn_classifier.predict_proba(X.iloc[test])[:, 1]
    knn_fpr, knn_tpr, knn_thresholds = roc_curve(y.iloc[test], knn_proba)
    knn_tprs.append(np.interp(mean_fpr, knn_fpr, knn_tpr))
    knn_tprs[-1][0] = 0.0
    knn_roc_auc = auc(knn_fpr, knn_tpr)
    knn_aucs.append(knn_roc_auc)
    ax.plot(knn_fpr, knn_tpr, lw=1, alpha=0.3,
            label='ROC fold %d (AUC = %0.2f)' % (i+1, knn_roc_auc))

# Random Forest Classifier ROC Curve
randomforest_classifier = RandomForestClassifier(n_estimators=10)
rf_tprs = []
rf_aucs = []
for i, (train, test) in enumerate(cv.split(X, y)):
    randomforest_classifier.fit(X.iloc[train], y.iloc[train])
    rf_proba = randomforest_classifier.predict_proba(X.iloc[test])[:, 1]
    rf_fpr, rf_tpr, rf_thresholds = roc_curve(y.iloc[test], rf_proba)
    rf_tprs.append(np.interp(mean_fpr, rf_fpr, rf_tpr))
    rf_tprs[-1][0] = 0.0
    rf_roc_auc = auc(rf_fpr, rf_tpr)
    rf_aucs.append(rf_roc_auc)
    ax.plot(rf_fpr, rf_tpr, lw=1, alpha=0.3,
            label='ROC fold %d (AUC = %0.2f)' % (i+1, rf_roc_auc))

# Plot the mean ROC curves
ax.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',
        label='Chance', alpha=.8)

knn_mean_tpr = np.mean(knn_tprs, axis=0)
knn_mean_tpr[-1] = 1.0
knn_mean_auc = auc(mean_fpr, knn_mean_tpr)
std_auc = np.std(knn_aucs)
ax.plot(mean_fpr, knn_mean_tpr, color='b',
        label=r'KNN Mean ROC (AUC = %0.2f $\pm$ %0.2f)' % (knn_mean_auc, std_auc),
        lw=2, alpha=.8)

rf_mean_tpr = np.mean(rf_tprs, axis=0)
rf_mean_tpr[-1] = 1.0
rf_mean_auc = auc(mean_fpr, rf_mean_tpr)
std_auc = np.std(rf_aucs)
ax.plot(mean_fpr, rf_mean_tpr, color='g',
        label=r'RF Mean ROC (AUC = %0.2f $\pm$ %0.2f)' % (rf_mean_auc, std_auc),
        lw=2, alpha=.8)

# Set the plot parameters
ax.set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05],
       title="Receiver operating characteristic example")
ax.legend(loc="lower right")
plt.show()

这段代码将绘制KNN分类器和随机森林分类器的ROC曲线，以及它们的平均曲线和AUC值。您需要将其与您的数据集和分类器参数一起使用。