Table of Contents
What is a good accuracy for a logistic regression model?
Sklearn has a cross_val_score object that allows us to see how well our model generalizes. So the range of our accuracy is between 0.62 to 0.75 but generally 0.7 on average.
Which significance test is used in logistic regression?
Wald statistic
Furthermore, we use the t-test to assess the significance of individual variables where simple regression is concerned. However, in the case of logistic regression, we use the Wald statistic to assess the significance of the independent variables.
How do you improve accuracy in logistic regression?
Hyperparameter Tuning – Grid Search – You can improve your accuracy by performing a Grid Search to tune the hyperparameters of your model. For example in case of LogisticRegression , the parameter C is a hyperparameter. Also, you should avoid using the test data during grid search. Instead perform cross validation.
How do you know if a logistic regression model is good?
It examines whether the observed proportions of events are similar to the predicted probabilities of occurence in subgroups of the data set using a pearson chi square test. Small values with large p-values indicate a good fit to the data while large values with p-values below 0.05 indicate a poor fit.
With chi-square contingency analysis, the independent variable is dichotomous and the dependent variable is dichotomous. Logistic regression is a more general analysis, however, because the independent variable (i.e., the predictor) is not restricted to a dichotomous variable.
How do you use logistic regression in testing data set?
I have a trained logistic regression model that I am applying to a testing data set. The dependent variable is binary (boolean). For each sample in the testing data set, I apply the logistic regression model to generates a \% probability that the dependent variable will be true. Then I record whether the acutal value was true or false.
How to find the accuracy with logistic regression in Python?
This is how we can find the accuracy with logistic regression: score = LogisticRegression.score(X_test, y_test) print(‘Test Accuracy Score’, score)
How do you calculate accaccuracy in logistic regression?
Accuracy is the proportion of correct predictions over total predictions. This is how we can find the accuracy with logistic regression: score = LogisticRegression.score (X_test, y_test) print (‘Test Accuracy Score’, score)
What is the prediction accuracy of displayr’s logistic regression?
The table below shows the prediction-accuracy table produced by Displayr’s logistic regression. At the base of the table you can see the percentage of correct predictions is 79.05\%. This tells us that for the 3,522 observations (people) used in the model, the model correctly predicted whether or not somebody churned 79.05\% of the time.