logistic-regression

来源： https://stackoverflow.com/questions/44004926/partial-fit-with-sgdclassifier-gives-fluctuating-accuracy

来源： https://stackoverflow.com/questions/44574679/python-deprecationwarning-elementwise-comparison-failed-this-will-raise-an

来源： https://stackoverflow.com/questions/44574679/python-deprecationwarning-elementwise-comparison-failed-this-will-raise-an

来源： https://stackoverflow.com/questions/63666436/how-to-write-cost-function-formula-from-andrew-ng-assignment-in-octave

来源： https://stackoverflow.com/questions/47185586/logistic-regression-valueerror-classification-metrics-cant-handle-a-mix-of-c

来源： https://stackoverflow.com/questions/63360751/specifying-random-effects-for-repeated-measures-in-logistic-mixed-model-in-r-lm

来源： https://stackoverflow.com/questions/63360751/specifying-random-effects-for-repeated-measures-in-logistic-mixed-model-in-r-lm

问题 def train(X_train,y_train,X_test,y_test,epochs,alpha,eta0): w,b = initialize_weights(X_train[0]) loss_test=[] N=len(X_train) for i in range(0,epochs): print(i) for j in range(N-1): grad_dw=gradient_dw(X_train[j],y_train[j],w,b,alpha,N) grad_db=gradient_db(X_train[j],y_train[j],w,b) w=np.array(w)+(alpha*(np.array(grad_dw))) b=b+(alpha*(grad_db)) predict2 = [] for m in range(len(y_test)): z=np.dot(w[0],X_test[m])+b if sigmoid(z) == 0: # sigmoid(w,x,b) returns 1/(1+exp(-(dot(x,w)+b))) predict2

问题 I am using Anaconda and I am trying logistic regression. After loading training data set and performed the regression. Then I got the following warning message. train_cols = data.columns[1:] logit = sm.Logit(data['harmful'], data[train_cols]) result = logit.fit() Warning: Maximum number of iterations has been exceeded. Current function value: 0.000004 Iterations: 35 C:\Users\dell\Anaconda\lib\site-packages\statsmodels\base\model.py:466: ConvergenceWarning: Maximum Likelihood optimization

问题 I'm trying to understand the difference between RidgeClassifier and LogisticRegression in sklearn.linear_model . I couldn't find it in the documentation. I think I understand quite well what the LogisticRegression does.It computes the coefficients and intercept to minimise half of sum of squares of the coefficients + C times the binary cross-entropy loss , where C is the regularisation parameter. I checked against a naive implementation from scratch, and results coincide. Results of