<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[ro_ran的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/ro_ran</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; ro_ran]]></copyright><item><title><![CDATA[【sklearn学习打卡】决策树]]></title><link>https://blog.youkuaiyun.com/ro_ran/article/details/122138750</link><guid>https://blog.youkuaiyun.com/ro_ran/article/details/122138750</guid><author>ro_ran</author><pubDate>Sat, 25 Dec 2021 01:49:58 +0800</pubDate><description><![CDATA[]]></description><category></category></item><item><title><![CDATA[【sklearn学习】SVM]]></title><link>https://blog.youkuaiyun.com/ro_ran/article/details/122019471</link><guid>https://blog.youkuaiyun.com/ro_ran/article/details/122019471</guid><author>ro_ran</author><pubDate>Sun, 19 Dec 2021 00:33:49 +0800</pubDate><description><![CDATA[线性SVM
model_linear = svm.SVC(kernel='linear', C = 0.001)
model_linear.fit(data, label) # 训练
Z = model_linear.predict(np.c_[xx.ravel(), yy.ravel()]) # 预测

多项式SVM
model_poly = svm.SVC(C=0.0001, kernel='poly', degree=degree) # 多项式核
model_poly.fit(data, label)]]></description><category></category></item><item><title><![CDATA[【sklearn学习】linear regression & logistic regression]]></title><link>https://blog.youkuaiyun.com/ro_ran/article/details/121961482</link><guid>https://blog.youkuaiyun.com/ro_ran/article/details/121961482</guid><author>ro_ran</author><pubDate>Wed, 15 Dec 2021 21:04:41 +0800</pubDate><description><![CDATA[linear regression
from sklearn.linear_model import LinearRegression # 导入线性回归模型
model = LinearRegression() # 定义模型
model.fit(X_train, y_train) # 训练模型
print("输出参数w：",model.coef_) # 输出模型参数w
print("输出参数b：",model.intercept_) # 输出参数b

polynomial regression
np.ran]]></description><category></category></item></channel></rss>