Sage-Tips

How do you improve model 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 calculate sensitivity in logistic regression?

Sensitivity = d/(c+d): The proportion of observed positives that were predicted to be positive.

How do you improve regression accuracy?

8 Methods to Boost the Accuracy of a Model

1. Add more data. Having more data is always a good idea.
2. Treat missing and Outlier values.
3. Feature Engineering.
4. Feature Selection.
5. Multiple algorithms.
6. Algorithm Tuning.
7. Ensemble methods.

How do you improve logistic regression?

Boosting works by sequentially applying a classifica- tion algorithm to reweighted versions of the training data and then taking a weighted majority vote of the sequence of classifiers thus produced. For many classification algorithms, this simple strategy results in dramatic improvements in performance.

Does logistic regression maximize accuracy?

Therefore, the logistic regression does its job of maximizing the joint probability. However, the model accuracy is now only 0.5, since only one data point (0.99) is correctly classified.

What is a good accuracy for logistic regression?

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.

What is accuracy 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)

Can boosting be applied to logistic regression?

GRADIENT BOOSTING MACHINES – THEORY Usually the gradient boosting method is used of decision tree models, however any model can be used in this process, such as a logistic regression.

Is logistic regression unbiased?

It turns out that because of the special symmetry property of the odds ratio measure which lies at the heart of logistic regression, a logistic regression complete case/records analysis can be unbiased for the association of a variable of interest (e.g. exposure) adjusted for a number of other covariates (e.g. …

How can I improve the accuracy of my regression model?

As some others have suggested, moving to a more complex algorithm may provide an increase in accuracy. If you were to stick with logistic regression, perhaps oversampling your smaller cohort may help to enrich your predictive performance. This could be achieved using a weighted logistic regression weighted logistic regression.

How to improve the accuracy of logisticregression?

4. 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 to tune the hyperparameters of a logistic regression model?

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.

How to avoid using test data in logistic regression?

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. Use your test data only to report the final numbers for your final model.