from sklearn.preprocessing import StandardScaler scaler = StandardScaler() scaler.fit(X) X_s= scaler.transform(X) X_s[:3] from sklearn.ensemble import RandomForestRegressor model = RandomForestRegressor(n_estimators=5000, max_features=int(X.shape[1] / 3), random_state=0) model.fit(X_s,y) model.score(X_s,y) pred = model.predict(X_s) plt.scatter(pred, y, alpha=0.6) w = np.linspace(min(pred), max(pred), 100) plt.plot(w, w) plt.xlabel('pred') plt.ylabel('y_test') plt.title('Comparison of GDP fitted value and true value') print(model.feature_importances_) sorted_index = model.feature_importances_.argsort() plt.barh(range(X.shape[1]), model.feature_importances_[sorted_index]) plt.yticks(np.arange(X.shape[1]),X.columns[sorted_index],fontsize=14) plt.xlabel('X Importance',fontsize=12) plt.ylabel('covariate X',fontsize=12) plt.title('Importance Ranking Plot of Covariate ',fontsize=15) plt.tight_layout()

# K-means模型训练 from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_scaled = scaler.fit_transform(X)

这段代码是使用sklearn中的StandardScaler对数据进行标准化处理，然后再用K-means模型进行训练。在K-means模型中，为了保证各个特征的重要性相同，需要对数据进行标准化处理，使得数据的均值为0，方差为1。这可以...

1. 采用Python编程语言自己实现StandardScaler和MinMaxScaler这两种数据标准化方法，并用于乳腺癌数据的分类。要求模型预测的准确率结果必须与以下sklearn的一样： from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split cancer = load_breast_cancer() X = cancer.data y = cancer.target X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=0.3) from sklearn.preprocessing import MinMaxScaler from sklearn.preprocessing import StandardScaler scaler = StandardScaler() #scaler = MinMaxScaler() scaler.fit(X_train) X_train_scaled = scaler.transform(X_train) X_test_scaled = scaler.transform(X_test) from sklearn.linear_model import LogisticRegression logreg=LogisticRegression() logreg.fit(X_train_scaled, y_train) y_pred = logreg.predict(X_test_scaled) from sklearn.metrics import accuracy_score accuracy_score(y_test, y_pred)

X_train_scaled = scaler.transform(X_train) X_test_scaled = scaler.transform(X_test) # 使用逻辑回归进行分类 from sklearn.linear_model import LogisticRegression logreg = LogisticRegression() logreg....

from sklearn.preprocessing import StandardScaler scaler = StandardScaler() data_selected = scaler.fit_transform(data_selected)

好的，我现在需要回答用户关于sklearn中StandardScaler的fit_transform使用方法的问题。首先，我得回忆一下StandardScaler的基本作用，它是用来做数据标准化的，也就是将数据按特征（列）进行均值为0，标准差为1的...

# 加载数据集from sklearn.datasets import load_irisiris = load_iris()X = iris.data# 数据预处理from sklearn.preprocessing import StandardScalerscaler = StandardScaler()X_scaled = scaler.fit_transform(X)# 使用K-means算法聚类from sklearn.cluster import KMeanskmeans = KMeans(n_clusters=3, random_state=42)kmeans.fit(X_scaled)y_pred = kmeans.predict(X_scaled)# 使用轮廓系数评估聚类方法的优劣from sklearn.metrics import silhouette_scoresilhouette_score(X_scaled, y_pred)

X_scaled = scaler.fit_transform(X) # 使用K-means算法聚类 from sklearn.cluster import KMeans kmeans = KMeans(n_clusters=3, random_state=42) kmeans.fit(X_scaled) y_pred = kmeans.predict(X_scaled) # ...

from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_scaled = scaler.fit_transform(X)报错Input contains NaN, infinity or a value too large for dtype('float64').

这个错误提示表明数据集中存在缺失...X_scaled = scaler.fit_transform(df) 这样处理之后，就可以对数据进行标准化了。如果数据集中存在极大的数值，可以尝试对这些数值进行截断或者缩放，以避免数值过大的问题。

import pandas as pd from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA file_path = r'E:\XuJunjie\Dasanxia\大数据实验\实验3\实验三\1. 城市排名.txt' data = pd.read_csv(file_path,sep=',') X = data.values.reshape(-1,1) scaler = StandardScaler() X_scaled = scaler.fit_transform(X) pca = PCA(n_components=2) X_pca = pca.fit_transform(X_scaled) print(pca.components_) print(pca.explained_variance_ratio_)

然后使用sklearn库中的StandardScaler()方法对X进行标准化处理，即均值为0，方差为1。接着使用PCA方法对标准化后的数据进行降维处理，将其降到2维，即将原来的数据压缩到一个二维的平面内。最后输出了降维后的主成分...

from sklearn.preprocessing import StandardScaler scaler = StandardScaler() scaler.fit(heart.drop("target", axis =1)) heart_scaled = scaler.transform(heart.drop("target", axis = 1)) heart_scaled = pd.DataFrame(heart_scaled, columns = heart.columns[:-1]) X = heart_scaled y = heart["target"] from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=101)

首先，从sklearn.preprocessing导入StandardScaler类，该类将特征数据标准化，使得每个特征的均值为0，方差为1。然后，用scaler.fit()方法拟合数据，以计算每个特征的均值和标准差。接下来，用scaler.transform()...

from sklearn.cluster import DBSCAN from sklearn.datasets import load_iris from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt # 加载数据集 iris = load_iris() X = iris.data # 数据预处理，标准化数据 scaler = StandardScaler() X = scaler.fit_transform(X) # 使用DBSCAN聚类算法 dbscan = DBSCAN(eps=1, min_samples=6) y_pred = dbscan.fit_predict(X) # 输出聚类结果 #print('聚类结果:', y_pred) plt.scatter(X[:, 0], X[:, 1], c=y_pred) plt.show()

这段代码是使用Python中的scikit-learn库实现了DBSCAN聚类算法对鸢尾花数据集进行聚类。首先，代码加载了鸢尾花数据集，然后对数据进行了标准化处理。接着，创建了一个DBSCAN聚类算法对象，其中eps参数表示邻域半径...

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import pandas as pd from sklearn.utils import shuffle from sklearn.preprocessing import scale df = pd.read_csv("C:\\boston.csv",header=0) ds = df.values from sklearn.datasets import load_boston boston = load_boston() X = boston.data y = boston.target from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense model = Sequential([ Dense(64, activation='relu', input_shape=(X_train.shape[1],)), Dense(64, activation='relu'), Dense(64, activation='relu'), Dense(1) ]) model.compile(loss='mean_squared_error', optimizer='adam') history = model.fit(X_train, y_train, validation_split=0.1, epochs=100, batch_size=32) from sklearn.metrics import mean_squared_error y_pred = model.predict(x_test)mse = mean_squared_error(y_test, y_pred)print('MSE:’, mse) import matplotlib.pyplot as plt plt.plot(history.history['accuracy'], label='train') plt.plot(history.history['val_accuracy'], label='validation') plt.legend() plt.show()

X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) 定义一个包含4个Dense层的神经网络模型： from tensorflow.keras.models import Sequential from tensorflow.keras.layers ...

from sklearn.preprocessing import StandardScaler scaler = StandardScaler() scaler.fit(X) X_s= scaler.transform(X) X_s[:3] from sklearn.ensemble import RandomForestRegressor model = RandomForestRegressor(n_estimators=5000, max_features=int(X.shape[1] / 3), random_state=0) model.fit(X_s,y) model.score(X_s,y) pred = model.predict(X_s) plt.scatter(pred, y, alpha=0.6) w = np.linspace(min(pred), max(pred), 100) plt.plot(w, w) plt.xlabel('pred') plt.ylabel('y_test') plt.title('Comparison of GDP fitted value and true value') print(model.feature_importances_) sorted_index = model.feature_importances_.argsort() plt.barh(range(X.shape[1]), model.feature_importances_[sorted_index]) plt.yticks(np.arange(X.shape[1]),X.columns[sorted_index],fontsize=14) plt.xlabel('X Importance',fontsize=12) plt.ylabel('covariate X',fontsize=12) plt.title('Importance Ranking Plot of Covariate ',fontsize=15) plt.tight_layout()

首先，通过导入StandardScaler库，使用fit方法对特征X进行标准化处理，然后使用transform方法对特征进行转换得到X_s。接着，导入RandomForestRegressor库，创建一个包含5000个决策树的随机森林回归...

from sklearn.preprocessing import StandardScaler # 对数据进行标准化处理 scaler = StandardScaler() X = scaler.fit_transform(df.drop('label', axis=1)) y = df['label']

这段代码中，使用了StandardScaler()函数对数据进行了标准化处理。标准化是一种数据预处理方法...X_test = scaler.transform(X_test) 这样可以确保测试数据集和训练数据集使用相同的均值和标准差进行标准化处理。

from sklearn.preprocessing import StandardScaler scaler = StandardScaler() df_scaled = pd.DataFrame(scaler.fit_transform(df), columns=df.columns) sns.heatmap(df_scaled, cmap='viridis')解释代码

from sklearn.preprocessing import StandardScaler - **作用**：从scikit-learn库中导入StandardScaler类，用于对数据进行标准化（Z-score标准化）。 - **标准化公式**： $$ z = \frac{x - \mu}{\sigma...

df = pd.read_csv("C:\\Users\\flx12\\Desktop\\大三下\\数据挖掘\\第二次上机作业\\Train.csv") X = df.drop('Segmentation', axis=1) y = df['Segmentation'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_train = scaler.fit_transform(X_train)

这段代码是用 Pandas 库...首先，使用 fit_transform() 函数对训练集进行标准化处理，然后使用 transform() 函数对测试集进行标准化处理。这个过程可以确保所有特征在同一尺度上，以便于模型更好地学习数据的特征。

from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split scaler = StandardScaler()

通过调用 scaler.fit(X) 来拟合数据集 X 的统计特性，然后 scaler.transform(X) 可以对原始数据进行标准化操作。 train_test_split 则是数据拆分函数，用于将给定的数据集划分为训练集和测试集两部分。通常...

Screenshot_20250709_163758_com.tencent.tmgp.pubgmhd.jpg

#数据标准化 from sklearn.preprocessing import StandardScaler scaler = StandardScaler() scaler.fit(X_train) X_train_s = scaler.transform(X_train) X_val_s = scaler.transform(X_val) test_s=scaler.transform(test)

from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X = scaler.fit_transform(X)

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#数据标准化 from sklearn.preprocessing import StandardScaler scaler = StandardScaler() scaler.fit(X_train) X_train_s = scaler.transform(X_train) X_val_s = scaler.transform(X_val) test_s=scaler.transform(test)

from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X = scaler.fit_transform(X)

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# K-means模型训练 from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_scaled = scaler.fit_transform(X)

from sklearn.preprocessing import StandardScaler scaler = StandardScaler() data_selected = scaler.fit_transform(data_selected)

from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_scaled = scaler.fit_transform(X)报错Input contains NaN, infinity or a value too large for dtype('float64').

from sklearn.preprocessing import StandardScaler # 对数据进行标准化处理 scaler = StandardScaler() X = scaler.fit_transform(df.drop('label', axis=1)) y = df['label']

from sklearn.preprocessing import StandardScaler scaler = StandardScaler() df_scaled = pd.DataFrame(scaler.fit_transform(df), columns=df.columns) sns.heatmap(df_scaled, cmap='viridis')解释代码

from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split scaler = StandardScaler()

Screenshot_20250709_163758_com.tencent.tmgp.pubgmhd.jpg

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