Can logistic regression be used for feature selection?

Can logistic regression be used for feature selection?

you can select the best features based on statistical significance of logistic regression model coefficients. PCA is technique often employed to simultaneosly perform feature selection and dimensionality reduction in regression problems.

Does XGBoost perform feature selection?

Feature Selection with XGBoost Feature Importance Scores Feature importance scores can be used for feature selection in scikit-learn. This is done using the SelectFromModel class that takes a model and can transform a dataset into a subset with selected features.

How many features can be used for logistic regression?

It’s not some rule that specifies how many features you are permitted to use. The Rule of 10 is descriptive, not prescriptive, and it’s an approximate guideline: if the number of instances is much fewer than 10 times the number of features, you’re at especially high risk of overfitting, and you might get poor results.

How do you improve logistic regression results?

1 Answer

1. Feature Scaling and/or Normalization – Check the scales of your gre and gpa features.
2. Class Imbalance – Look for class imbalance in your data.
3. Optimize other scores – You can optimize on other metrics also such as Log Loss and F1-Score.

Which is an example of an XGBoost model?

Tying this together, the complete example of evaluating an XGBoost model on the housing regression predictive modeling problem is listed below. Running the example evaluates the XGBoost Regression algorithm on the housing dataset and reports the average MAE across the three repeats of 10-fold cross-validation.

Which is the best model selection in logistic regression?

Not surprising with the levels of model selection (Logistic Regression, Random Forest, XGBoost), but in my Data Science-y mind, I had to dig deeper, particularly in Logistic Regression. Let’s reverse gears for those already about to h i t the back button.

What should my XGBoost score be in logistic regression?

As a side note: my XGBoost selected ( kills, walkDistance, longestKill, weaponsAcquired, heals, boosts, assists, headshotKills) which resulted (after hyperparameter tuning) in a 99.4% test accuracy score.

Can you install XGBoost as a standalone library?

XGBoost can be installed as a standalone library and an XGBoost model can be developed using the scikit-learn API. The first step is to install the XGBoost library if it is not already installed. This can be achieved using the pip python package manager on most platforms; for example: