This is now implemented as part of scikit-learn version 0.18. You can pass a ‘method’ string parameter to the cross_val_predict method. Documentation is here. Example: proba = cross_val_predict(logreg, X, y, cv=cv, method=’predict_proba’) Also note that this is part of the new sklearn.model_selection package so you will need this import: from sklearn.model_selection import cross_val_predict
Source: my own notes taken during Standford’s Machine Learning course in Coursera, by Andrew Ng. All credits to him and this organization. The course is freely available for anybody to be taken at their own pace. The images are made by myself using LaTeX (formulas) and R (graphics). Hypothesis function Logistic regression is used when … Read more
take a look at the documentations (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html), offset coefficient isn’t stored by lr.coef_ coef_ array, shape = [n_classes-1, n_features] Coefficient of the features in the decision function. coef_ is readonly property derived from raw_coef_ that follows the internal memory layout of liblinear. intercept_ array, shape = [n_classes-1] Intercept (a.k.a. bias) added to the decision function. … Read more
I tried to upgrade my scikit-learn using the below command, still, that didn’t solve the AttributeError: ‘str’ object has no attribute ‘decode’ issue pip install scikit-learn -U Finally, below code snippet solved the issue, add the solver as liblinear model = LogisticRegression(solver=”liblinear”)
4.65761066e-03 + 9.95342389e-01 = 1 9.75851270e-01 + 2.41487300e-02 = 1 9.99983374e-01 + 1.66258341e-05 = 1 The first column is the probability that the entry has the -1 label and the second column is the probability that the entry has the +1 label. Note that classes are ordered as they are in self.classes_. If you would … Read more
There is a little trick that I use, instead of using model.predict(test_data) use model.predict_proba(test_data). Then use a range of values for thresholds to analyze the effects on the prediction; pred_proba_df = pd.DataFrame(model.predict_proba(x_test)) threshold_list = [0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,.7,.75,.8,.85,.9,.95,.99] for i in threshold_list: print (‘\n******** For i = {} ******’.format(i)) Y_test_pred = pred_proba_df.applymap(lambda x: 1 if x>i else 0) … Read more
The warning means what it mainly says: Suggestions to try to make the solver (the algorithm) converges. lbfgs stand for: “Limited-memory Broyden–Fletcher–Goldfarb–Shanno Algorithm”. It is one of the solvers’ algorithms provided by Scikit-Learn Library. The term limited-memory simply means it stores only a few vectors that represent the gradients approximation implicitly. It has better convergence … Read more
Regularization is applying a penalty to increasing the magnitude of parameter values in order to reduce overfitting. When you train a model such as a logistic regression model, you are choosing parameters that give you the best fit to the data. This means minimizing the error between what the model predicts for your dependent variable … Read more
One of the simplest options to get a feeling for the “influence” of a given parameter in a linear classification model (logistic being one of those), is to consider the magnitude of its coefficient times the standard deviation of the corresponding parameter in the data. Consider this example: import numpy as np from sklearn.linear_model import … Read more