The threshold can be set using clf.predict_proba() for example: from sklearn.tree import DecisionTreeClassifier clf = DecisionTreeClassifier(random_state = 2) clf.fit(X_train,y_train) # y_pred = clf.predict(X_test) # default threshold is 0.5 y_pred = (clf.predict_proba(X_test)[:,1] >= 0.3).astype(bool) # set threshold as 0.3
Yes, there is attribute coef_ for SVM classifier but it only works for SVM with linear kernel. For other kernels it is not possible because data are transformed by kernel method to another space, which is not related to input space, check the explanation. from matplotlib import pyplot as plt from sklearn import svm def … Read more
You get this error because your model is on the GPU, but your data is on the CPU. So, you need to send your input tensors to the GPU. inputs, labels = data # this is what you had inputs, labels = inputs.cuda(), labels.cuda() # add this line Or like this, to stay consistent with … Read more
While online algorithms for SVMs do exist, it has become important to specify if you want kernel or linear SVMs, as many efficient algorithms have been developed for the special case of linear SVMs. For the linear case, if you use the SGD classifier in scikit-learn with the hinge loss and L2 regularization you will … Read more
In mathematical sense you need to set: SVC(kernel=”linear”, **kwargs) # by default it uses RBF kernel and LinearSVC(loss=”hinge”, **kwargs) # by default it uses squared hinge loss Another element, which cannot be easily fixed is increasing intercept_scaling in LinearSVC, as in this implementation bias is regularized (which is not true in SVC nor should be … Read more
Don’t let the name confuse you. The layer tf.keras.layers.Conv1D needs the following shape: (time_steps, features). If your dataset is made of 10,000 samples with each sample having 64 values, then your data has the shape (10000, 64), which is not directly applicable to the tf.keras.layers.Conv1D layer. You are missing the time_steps dimension. What you can … Read more
GridSearchCV as @Gauthier Feuillen said is used to search best parameters of an estimator for given data. Description of GridSearchCV:- gcv = GridSearchCV(pipe, clf_params,cv=cv) gcv.fit(features,labels) clf_params will be expanded to get all possible combinations separate using ParameterGrid. features will now be split into features_train and features_test using cv. Same for labels Now the gridSearch estimator … Read more
There are two basic conditions under which MatrixFactorizationMode.predictAll may return a RDD with lower number of items than the input: user is missing in the training set. product is missing in the training set. You can easily reproduce this behavior and check that it is is not dependent on the way how RDD has been … Read more