I’m currently using sklearn’s Ridge classifier, and am looking to ensemble this classifier with classifiers from sklearn and other libraries. In order to do this, it would be ideal to extract the probability that a given input belongs to each class in a list of classes. Currently, I’m zipping the classes with the output of model.decision_function(x), but this returns the
Tag: scikit-learn
Why GridSearchCV spends more than 50% time on {method ‘acquire’ of ‘thread.lock’ objects}?
Recently I am tuning up some of my machine learning pipeline. I decided to take advantage of my multicore processor. And I ran cross-validation with param n_jobs=-1. I also profiled it and what was suprise for me: the top function was: I was not sure if it was my fault due to operations I do in Pipeline. So I decided
sklearn plot confusion matrix with labels
I want to plot a confusion matrix to visualize the classifer’s performance, but it shows only the numbers of the labels, not the labels themselves: How can I add the labels (health, business..etc) to the confusion matrix? Answer As hinted in this question, you have to “open” the lower-level artist API, by storing the figure and axis objects passed by
Ensamble methods with scikit-learn
Is there any way to combine different classifiers into one in sklearn? I find sklearn.ensamble package. It contains different models, like AdaBoost and RandofForest, but they use decision trees under the hood and I want to use different methods, like SVM and Logistic regression. Is it possible with sklearn? Answer Do you just want to do majority voting? This is
Principal Component Analysis (PCA) in Python
I have a (26424 x 144) array and I want to perform PCA over it using Python. However, there is no particular place on the web that explains about how to achieve this task (There are some sites which just do PCA according to their own – there is no generalized way of doing so that I can find). Anybody