Simple Linear Regression with Sklearn To demonstrate simple linear regression using the sklearn library, we'll use a California house price prediction dataset from Kaggle. Importing Libraries import pandas as pd
def svm_cross_validation(train_x, train_y): from sklearn.grid_search import GridSearchCV from sklearn.svm import SVC model = SVC(kernel='rbf', probability=True) param_grid = {'C': [1e-3, 1e-2, 1e-1, 1, 10, 100, 1000], 'gamma': [0.001, 0.0001]} grid_search = GridSearch...
from sklearn.linear_model import LogisticRegression from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.cross_validation import cross_val_score 1. 2. 3. 4. 5. 6....
sklearn是python的一个包,也是机器学习中常用的一个模块,里面封装了很多机器学习的算法,不需要对机器学习算法的实现,只需要简单地调用sklearn里相对应的模块即可。 机器学习任务通常包括分类classification、回归Regression,常用的分类器包括SVM、KNN、贝叶斯、线性回归、逻辑回归、决策树、随机森林、xgboost、GBDT、boosting...
然后，我们使用sklearn中的线性回归模型进行拟合和预测。# 导入线性回归模型from sklearn.linear_model import LinearRegression# 创建线性回归模型对象model = LinearRegression()# 在训练集上拟合模型model.fit(X_train, y_train)# 在测试集上进行预测y_pred = model.predict(X_test)print(y_pred.shape)print(y...
from sklearn import linear_model linereg01= linear_model.LinearRegression() #生成一个线性回归实例 # 分割模型为训练集与测试集(9:1) X_train,X_test,y_train,y_test= model_selection.train_test_split( boston.data,boston.target,test_size=0.1,random_state=42 ...
from sklearn.linear_model import LinearRegression import numpy as np # Create a dataset x = np.array([5, 15, 25, 35, 45, 55]).reshape((-1, 1)) y = np.array([5, 20, 14, 32, 22, 38]) # Create a model …
The L2 norm term is weighted by a regularization parameter alpha: if alpha=0 then you recover the Ordinary Least Squares regression model. The larger the alpha the higher the smoothness constraint. Below you can see the approximation of a sklearn.linear_model.RidgeRegression estimator fitting a ...
sklearn 的 LinearRegression模块 fromsklearn.linear_modelimportLinearRegression#导入LinearRegression模块(普通最小二乘线性回归)#LinearRegression 拟合线性模型,系数 w = (w1, …, wp) 最小化观察目标之间的残差平方和 数据集#以及线性近似预测的目标。LinearRegression(fit_intercept = True,normalize = False,...
feature_selection import RFE from sklearn.linear_model import LinearRegression def excel_one_line_to_list(): X = pd.read_excel("G:\毕业论文\B数据集\水稻稻叶瘟\data1.xls", usecols=[0,1,2,3,4,5],names=None) names = X.columns.tolist() X = np.array(X) X = X.tolist() Y =...