plt.title("Classifier:KNN")

这是一个使用 Matplotlib 库给图表添加标题的示例。它的作用是在一个分类器的可视化结果上添加一个标题，标题为“Classifier:KNN”，表明该分类器是基于KNN算法实现的。具体的实现代码可能类似于：

import matplotlib.pyplot as plt

# 假设有一个名为“result”的分类器可视化结果
plt.imshow(result)
plt.title("Classifier:KNN")
plt.show()

相关问题

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。

from sklearn.datasets import make_blobs X, y = _____________________ #导入相应的matplotlib.pyplot库和numpy库 ______________________________ _______________________________ # 画出数据 __________________________ #创建画布 # 用scatter函数画出样本 ____________________________ ___________________________#并保存文件 ___________________________#显示png #导入KNeighborsClassifier库 ______________________________ _____ # 模型搭建与训练 ___________________ ___________________ # 预测[0, 2] X_sample = ________________ y_sample = __________________ print(y_sample) # kneighbors(self[, X, n_neighbors, …]) 获取某节点的k个近邻 neighbors = ___________________________ print("测试样本："+str(X_sample)) print("近邻样本为："+str(neighbors)) # 画出示意图 _______________ ____________________________________# 画出训练样本 # 画出测试样本点 _______________________________________________________ for i in neighbors[0]: plt.plot([X[i][0], X_sample[0][0]], [X[i][1], X_sample[0][1]], 'k--', linewidth=0.6) # 预测点与距离最近的5个样本的连线 plt.savefig('knn_predict.png') plt.show()

这段代码是使用Scikit-learn库中的KNeighborsClassifier算法进行K近邻分类的代码，步骤如下：

1. 从Scikit-learn库中导入make_blobs函数生成样本数据。
2. 导入matplotlib.pyplot库和numpy库。
3. 用scatter函数画出生成的样本数据。
4. 创建画布，并保存文件。
5. 导入KNeighborsClassifier库。
6. 创建KNeighborsClassifier对象，并使用fit方法进行模型训练。
7. 使用predict方法进行预测。
8. 画出测试样本点与距离最近的5个样本的连线，并保存文件。

下面是代码的具体实现：

from sklearn.datasets import make_blobs
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import numpy as np

# 生成样本数据
X, y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.6)

# 画出数据
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.get_cmap('bwr'), edgecolors='k')
plt.colorbar()
plt.title('Sample Data')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')

# 保存文件
plt.savefig('knn_data.png')
plt.show()

# 创建KNeighborsClassifier对象，并使用fit方法进行模型训练
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X, y)

# 预测[0, 2]
X_sample = [[0, 2]]
y_sample = knn.predict(X_sample)
print(y_sample)

# kneighbors(self[, X, n_neighbors, …]) 获取某节点的k个近邻
neighbors = knn.kneighbors(X_sample, return_distance=False)

# 画出示意图
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.get_cmap('bwr'), edgecolors='k')
plt.colorbar()
plt.title('KNN Classifier')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')

# 画出测试样本点
plt.scatter(X_sample[0][0], X_sample[0][1], marker='x', color='r', s=200)

# 画出预测点与距离最近的5个样本的连线
for i in neighbors[0]:
    plt.plot([X[i][0], X_sample[0][0]], [X[i][1], X_sample[0][1]], 'k--', linewidth=0.6)

# 保存文件
plt.savefig('knn_predict.png')
plt.show()