Okay, I’m just going to say starting out that I’m entirely new to SciKit-Learn and data science. But here is the issue and my current research on the problem. Code at the bottom. Summary I’m trying to do type recognition (like digits, for example) with a BernoulliRBM and I’m trying to find the correct parameters with GridSearchCV. However I don’t
Tag: machine-learning
Custom transformer for sklearn Pipeline that alters both X and y
I want to create my own transformer for use with the sklearn Pipeline. I am creating a class that implements both fit and transform methods. The purpose of the transformer will be to remove rows from the matrix that have more than a specified number of NaNs. The issue I am facing is how can I change both the X
Scikit-learn Ridge classifier: extracting class probabilities
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
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