X_test,y_train,y_test=train_test_split(X,y,train_size=0.7,test_size=0.3)'''初始化逻辑回归分类器,这里对类别不平衡问题做了处理'''cl=LogisticRegression(class_weight='balanced')'''利用训练数据进行逻辑回归分类器的训练'''cl=cl.fit(X_train,y_train)'''打印
Multiple / Adjusted R-Square: For one variable, the distinction doesn’t really matter. R-squared shows the amount of variance explained by the model. Adjusted R-Square takes into account the number of variables and is most useful for multiple-regression.然后是R方和调整的R方，R方为这个模型能...
feature_tmp.append(1) # x0 for i in range(len(lines) - 1): feature_tmp.append(float(lines[i])) feature.append(feature_tmp) label.append(float(lines[-1])) f.close() return np.mat(feature), np.mat(label).T def least_square(feature, label): '''最小二乘法 input: feature(mat)...
再尝试一下将esp-regression改为nu-regression试试: > svm.r3=svm(y~x, type = "nu-regression",kernel = "linear"); svm.r3 Call: svm(formula = y ~ x, type = "nu-regression",kernel = "linear") Parameters: SVM-Type: nu-regression SVM-Kernel: linear cost: 1 gamma: 1 nu: 0.5 Number...
SSR:Sum Square of Regression(由于建的模型不完全与数据一致而引起的变异程度的平方和) SST:Sum Square of Total(所有点的变异量的平方和) y_hat:为估计值,在模型线上; y_bar:为平均值,所有y求均值; y(i):即为实际的y的值 SSE:Sum Square of Error(自然误差浮动)(SSE+SSR=SST) ...
Python 不像 R 语言,在进行滚动回归操作时,没有现成可用的函数来处理。倘若使用传统的“for 循环”来进行回归,当样本数量在 1 万以上时,运算速度会非常缓慢。 R语言多元时间序列滚动预测:ARIMA、回归、ARIMAX模型分析 当需要为数据选择最合适的预测模型或方法时,预测者通常将可用的样本分成两部分:内样本(又称 "训...
1. 线性回归(Linear Regression) 2. 逻辑回归(Logistic Regression) 3. 决策树(Decision Tree) 4. SVM 5. 朴素贝叶斯(Naive Bayes) 6. K最近邻(kNN) 7. K均值算法(K-Means) 8. 随机森林(Random Forest) 9. 降维算法(Dimensionality Reduction Algorithms) ...
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