改进代码import numpy as np from sklearn.model_selection import train_test_split from sklearn.svm import LinearSVC from sklearn.svm import SVC from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score # 数据集 data = np.array([ ['1000025', 'b', 5, 1, 1, 1, 2, 1, 3, 1, 1], ['1002945', 'b', 5, 4, 4, 5, 7, 10, 3, 2, 1], ['1015425', 'b', 3, 1, 1, 1, 2, 2, 3, 1, 1], ['1016277', 'b', 6, 8, 8, 1, 3, 4, 3, 7, 1], ['1017023', 'b', 4, 1, 1, 3, 2, 1, 3, 1, 1], ['1017122', 'm', 8, 10, 10, 8, 7, 10, 9, 7, 1], ['1018099', 'b', 1, 1, 1, 1, 2, 10, 3, 1, 1], ['1018561', 'b', 2, 1, 2, 1, 2, 1, 3, 1, 1] ]) # 将字符型数据转换为数值型数据 data[data == 'b'] = 0 data[data == 'm'] = 1 data = data.astype(float) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(data[:, 2:], data[:, 1], test_size=0.3, random_state=42) # 创建支持向量机分类器 # clf = SVC(kernel='linear', random_state=42) clf = LinearSVC(random_state=42) clf.fit(X_train, y_train) clf.fit(X_test, y_test) # 对测试集进行预测 y_pred = clf.predict(X_test) # 计算分类器的准确性 confusion = confusion_matrix(y_test, y_pred) accuracy = accuracy_score(y_test, y_pred) precision = precision_score(y_test, y_pred) recall = recall_score(y_test, y_pred) print('混淆矩阵：\n', confusion) print('准确率：', accuracy) print('精确率：', precision) print('召回率：', recall)

在Python中想运行from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random怎么操作

from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt ...

优化这段代码# import modules 导入模块 from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random

from sklearn.model_selection import train_test_split, StratifiedShuffleSplit, StratifiedKFold, GridSearchCV, LeaveOneOut, cross_val_predict, KFold from sklearn.decomposition import PCA from sklearn....

import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.svm import SVC from sklearn.model_selection import train_test_split，这些程序是干啥用的

具体来说，该程序使用load_iris函数加载鸢尾花数据集，然后使用train_test_split函数将数据集随机划分为训练集和测试集。接着，使用SVC函数创建一个支持向量机分类器，并使用训练集进行拟合。最后，使用测试集进行...

import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.metrics import classification_report from sklearn.metrics import confusion_matrix

这段代码是在 Python 中导入了一些常用的机器学习库和模块，包括 pandas、numpy、matplotlib、sklearn 等。其中： - pandas 是 Python 中常用的数据分析库，可以用来读取和处理数据； - numpy 是 Python 中常用的...

运行下面代码需要安装哪些包from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random

运行上述代码需要安装以下Python包： - scikit-learn：提供了机器学习算法和工具，包括模型选择、特征提取、预处理等。可以使用pip install scikit-learn进行安装。 - pandas：提供了高性能、易于使用的数据结构...

from sklearn import svm import pandas as pd import numpy as np from sklearn.model_selection import train_test_split data = pd.read_csv( 'final_data1.csv') Y = data.y X = data.drop('y', axis=1) X_train, X_test, y_train, y_test = train_test_split(X_norm, Y, test_size=0.2, random_state=42) clf = svm.SVC() clf.fit(X_train, y_train) y_pred = clf.predict(X_test) y_pred= np.round(y_pred) 对上述代码进行GridSearchCV网格搜索调参的代码

from sklearn.model_selection import train_test_split, GridSearchCV data = pd.read_csv('final_data1.csv') Y = data.y X = data.drop('y', axis=1) X_train, X_test, y_train, y_test = train_test_split(X, Y...

from sklearn.datasets import load_breast_cancer from sklearn.svm import SVC from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt import numpy as np #导入乳腺癌数据集 cancers = load_breast_cancer() #下载乳腺癌数据集 X = cancers.data #获取特征

这是一些Python代码，导入了sklearn的数据集、支持向量机模型和模型...其中，load_breast_cancer是一个用于乳腺癌分类的数据集，SVC是一种支持向量机分类器，train_test_split是将数据集随机分成训练集和测试集的方法。

import pandas as pd import numpy as np # 非线性支持向量机分类 from sklearn.svm import SVC # 标准化和处理分类型特征的库 from sklearn.preprocessing import StandardScaler, Binarizer from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt # 读取数据 data = pd.read_csv('primary_data.csv', index_col=0)

接着，代码导入了numpy、sklearn、和matplotlib.pyplot等库，这些库提供了处理数据、构建模型和可视化数据等功能。其中，numpy库提供了大量的数值计算函数和数据结构，sklearn库提供了机器学习算法和数据预处理工具...

import numpy as np import pandas as pd from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score from sklearn.metrics import confusion_matrix import matplotlib.pyplot as plt from termcolor import colored as cl import itertools from sklearn.preprocessing import StandardScaler from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC from sklearn.ensemble import RandomForestClassifier from xgboost import XGBClassifier from sklearn.neural_network import MLPClassifier from sklearn.ensemble import VotingClassifier # 定义模型评估函数 def evaluate_model(y_true, y_pred): accuracy = accuracy_score(y_true, y_pred) precision = precision_score(y_true, y_pred, pos_label='Good') recall = recall_score(y_true, y_pred, pos_label='Good') f1 = f1_score(y_true, y_pred, pos_label='Good') print("准确率:", accuracy) print("精确率:", precision) print("召回率:", recall) print("F1 分数:", f1) # 读取数据集 data = pd.read_csv('F:\数据\大学\专业课\模式识别\大作业\数据集1\data clean Terklasifikasi baru 22 juli 2015 all.csv', skiprows=16, header=None) # 检查数据集 print(data.head()) # 划分特征向量和标签 X = data.iloc[:, :-1] y = data.iloc[:, -1] # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 6. XGBoost xgb = XGBClassifier(max_depth=4) y_test = np.array(y_test, dtype=int) xgb.fit(X_train, y_train) xgb_pred = xgb.predict(X_test) print("\nXGBoost评估结果：") evaluate_model(y_test, xgb_pred)

5. 划分训练集和测试集：使用sklearn库的train_test_split()函数将数据集划分为训练集和测试集。 6. 使用XGBoost算法进行分类：使用XGBClassifier类构建XGBoost分类器，并使用fit()函数将训练集拟合到该分类器中。...

import numpy as np from sklearn.svm import SVC from sklearn.metrics import accuracy_score from sklearn.model_selection import train_test_split from PIL import Image # 加载数据集 X = [] y = [] for i in range(1000): img = Image.open(f"train/cats.{i}.jpg") img = img.resize((150, 150)) X.append(np.array(img)) y.append(0) for i in range(1000): img = Image.open(f"train/dog.{i}.jpg") img = img.resize((150, 150)) X.append(np.array(img)) y.append(1) X = np.array(X) y = np.array(y) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 将图像数据拉平 X_train = X_train.reshape(X_train.shape[0], -1) X_test = X_test.reshape(X_test.shape[0], -1) # 构建模型 model = SVC(kernel='linear') # 训练模型 model.fit(X_train, y_train) # 预测结果 y_pred = model.predict(X_test) # 计算准确率 acc = accuracy_score(y_test, y_pred) print(f"Accuracy: {acc}")

使用train_test_split函数将X和y分别划分为训练集和测试集，测试集大小为20%。 3. 将图像数据拉平。将每张150x150的图片转换成一维数组，方便后续模型训练。 4. 构建模型。使用SVC(kernel='linear')构建线性SVM...

优化这段代码from sklearn.neighbors import KNeighborsClassifier from sklearn.datasets import load_digits from sklearn import svm, metrics from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix from sklearn.metrics import plot_confusion_matrix import numpy as np #使绘图支持中文字符 from matplotlib import rcParams rcParams['font.family'] = 'SimHei' plt.rcParams['axes.unicode_minus'] = False # In[2]: digits = load_digits() data = digits.data print(data[0]) print(digits.images[0]) print(digits.target[0]) plt.imshow(digits.images[0]) plt.show() # In[3]: train_x, test_x, train_y, test_y = train_test_split(data, digits.target, test_size=0.3, random_state=82) print(train_x) clf = svm.SVC(kernel='linear') clf.fit(train_x, train_y) print("svm训练集得分: %.4lf" % clf.score(train_x, train_y)) print("svm测试集得分: %.4lf" % clf.score(test_x, test_y)) print(clf.predict(data)) plot_confusion_matrix(clf, test_x, test_y) plt.show() # In[5]: ################################################################# fig = plt.figure(figsize=(6,13)) for i in range(40): y_pred = clf.predict([data[i]]) plt.subplot(8,5,i+1) plt.imshow(digits.images[i], interpolation='none') plt.title("%d---->%d"%(digits.target[i],y_pred)) plt.show()

from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt from sklearn.metrics import plot_confusion_matrix import numpy as np # 使绘图支持中文字符 from matplotlib import ...

import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.svm import SVC from sklearn.metrics import accuracy_score # 加载鸢尾花数据集 iris = load_iris() X = iris.data y = iris.target # 数据可视化 - 处理前 plt.scatter(X[:, 0], X[:, 1], c=y) plt.xlabel('Sepal Length') plt.ylabel('Sepal Width') plt.title('Iris Dataset - Before Preprocessing') plt.show() # 划分数据集为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 创建并拟合支持向量机模型 model = SVC() model.fit(X_train, y_train) # 在测试集上进行预测 y_pred = model.predict(X_test) # 计算准确度 accuracy = accuracy_score(y_test, y_pred) print('Accuracy:', accuracy) # 数据可视化 - 处理后 plt.scatter(X_test[:, 0], X_test[:, 1], c=y_pred) plt.xlabel('Sepal Length') plt.ylabel('Sepal Width') plt.title('Iris Dataset - After Preprocessing') plt.show()

这段代码使用了支持向量机（SVM）算法对鸢尾花数据集进行分类，并且使用了数据可视化来展示分类效果。具体来说，代码中首先加载了鸢尾花数据集，然后对数据集进行了可视化处理，接着将数据集划分为训练集和测试集，...

请根据以下代码，补全并完成任务代码：作业：考虑Breast_Cancer-乳腺癌数据集总类别数为2 特征数为30 样本数为569（正样本212条，负样本357条）特征均为数值连续型、无缺失值 (1)使用GridSearchCV搜索单个DecisionTreeClassifier中max_samples,max_features,max_depth的最优值。 (2)使用GridSearchCV搜索BaggingClassifier中n_estimators的最佳值。 (3)考虑BaggingClassifier中的弱分类器使用SVC（可以考虑是否使用核函数），类似步骤(1),(2)，自己调参（比如高斯核函数的gamma参数,C参数），寻找最优分类结果。from sklearn.datasets import load_breast_cancer from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap ds_breast_cancer = load_breast_cancer() X=ds_breast_cancer.data y=ds_breast_cancer.target # draw sactter f1 = plt.figure() cm_bright = ListedColormap(['r', 'b', 'g']) ax = plt.subplot(1, 1, 1) ax.set_title('breast_cancer') ax.scatter(X[:, 0], X[:, 1], c=y, cmap=cm_bright, edgecolors='k') plt.show() #（1） from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import GridSearchCV from sklearn.preprocessing import StandardScaler # 数据预处理 sc = StandardScaler() X_std = sc.fit_transform(X) # 定义模型，添加参数 min_samples_leaf tree = DecisionTreeClassifier(min_samples_leaf=1) # 定义参数空间 param_grid = {'min_samples_leaf': [1, 2, 3, 4, 5], 'max_features': [0.4, 0.6, 0.8, 1.0], 'max_depth': [3, 5, 7, 9, None]} # 定义网格搜索对象 clf = GridSearchCV(tree, param_grid=param_grid, cv=5) # 训练模型 clf.fit(X_std, y) # 输出最优参数 print("Best parameters:", clf.best_params_) #（2） from sklearn.ensemble import BaggingClassifier # 定义模型 tree = DecisionTreeClassifier() bagging = BaggingClassifier(tree) # 定义参数空间 param_grid = {'n_estimators': [10, 50, 100, 200, 500]} # 定义网格搜索对象 clf = GridSearchCV(bagging, param_grid=param_grid, cv=5) # 训练模型 clf.fit(X_std, y) # 输出最优参数 print("Best parameters:", clf.best_params_)

from sklearn.model_selection import GridSearchCV from sklearn.preprocessing import StandardScaler # 数据预处理 sc = StandardScaler() X_std = sc.fit_transform(X) # 定义弱分类器 svc = SVC(kernel='rbf'...

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.svm import SVC from sklearn.metrics import accuracy_score #读取数据 data = pd.read_csv('JD_消费者数据20180201-20180415.csv') #删除不需要的列 data = data.drop(['customer_id', 'product_id', 'action_date', 'action_id'], axis=1) #处理时间数据，将日期转为距离当前日期的天数 data['customer_register_date'] = (pd.to_datetime('2018-04-15') - pd.to_datetime(data['customer_register_date'])).dt.days data['product_market_date'] = (pd.to_datetime('2018-04-15') - pd.to_datetime(data['product_market_date'])).dt.days data['shop_register_date'] = (pd.to_datetime('2018-04-15') - pd.to_datetime(data['shop_register_date'])).dt.days #删除缺失值所在的行 data.dropna(inplace=True) #将分类变量转为数值变量，使用One-hot编码 data = pd.get_dummies(data, columns=['age_range', 'gender', 'brand', 'category', 'shop_category']) #将目标变量转为数值变量，PageView为0，Order为1 data['type'] = data['type'].apply(lambda x: 0 if x == 'PageView' else 1) #读取前五行 data.head(5)

这段代码使用了Python中的Pandas、NumPy、Matplotlib、Seaborn和Scikit-learn库来读取一个csv文件并对数据进行处理。其中，数据处理部分包括删除不需要的列、将时间数据转为距离当前日期的天数、删除缺失值所在的行...

SVM_svmpython_PYTHON案例_python_SVM_

from sklearn.model_selection import train_test_split from sklearn.svm import SVC from sklearn.metrics import accuracy_score import matplotlib.pyplot as plt 2. **加载数据集** 这里可以使用sklearn...

svmMLiA.zip_35w

from sklearn.model_selection import train_test_split from sklearn.svm import SVC from sklearn.metrics import accuracy_score 接着，我们加载数据集。假设我们有一个二分类问题的数据集iris： python...

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from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC from sklearn.metrics import classification_report, confusion_matrix ...

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