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Is cross-validation necessary for logistic regression?
In general cross validation is always needed when you need to determine the optimal parameters of the model, for logistic regression this would be the C parameter.
When should I use cross-validation?
Cross-validation is primarily used in applied machine learning to estimate the skill of a machine learning model on unseen data. That is, to use a limited sample in order to estimate how the model is expected to perform in general when used to make predictions on data not used during the training of the model.
What is cross-validation in logistic regression?
Cross-Validation Explained Cross-validation is a method that can estimate the performance of a model with less variance than a single ‘train-test’ set split. It works by splitting the dataset into k-parts (i.e. k = 5, k = 10). The testing group rotates each time the model is trained.
When to use cross validation in logistic regression?
For your last question: After you have found the best model doing cross-validation, and you have decided that this model is going to be used in production, you should train the model on all data available, so that you get the most accurate estimates possible. Thanks for contributing an answer to Cross Validated!
How to use cross validation in machine learning?
I’m looking for the equivalent: import pandas as pd from sklearn.cross_validation import cross_val_score from sklearn.linear_model import LogisticRegression ## Assume pandas dataframe of dataset and target exist. scores = cross_val_score (LogisticRegression (),dataset,target,cv=10) print (scores) And now I’m stuck.
What are the changes to CV for logistic regression?
The changes I made were to make it a logit (logistic) model, add modeling and prediction, store the CV’s results, and to make it a fully working example. Also note that there are many packages and functions you could use, including cv.glm () from boot.
How to evaluate a logistic regression in Python?
Specifically, what it does is the following: It divides your dataset in to n folds and in each iteration it leaves one of the folds out as the test set and trains the model on the rest of the folds ( n-1 folds). So, in the end you will get predictions for the entire data.