We can compare two algorithms in different categories —
|Criteria||Logistic Regression||Decision Tree Classificationtr>|
|Interpretability||Less interpretable||More interpretable|
|Decision Boundaries||Linear and single decision boundary||Bisects the space into smaller spaces|
|Ease of Decision Making||A decision threshold has to be set||Automatically handles decision making td >||Overfitting||Not prone to overfitting||Prone to overfitting|
|Robustness to noise||Robust to noise||Majorly affected by noise|
|Scalability||Requires a large enough training set||Can be train ed on a small training set|
As a simple experiment, we run two models on the same dataset and compare their characteristics.
Step 1: Import the required libraries
Step 2: Read and clear the dataset
Step 3: Train and evaluate the Logisitc regression model
Step 4: Train and evaluate the decision tree classifier model
Comparing the scores, we see that the logistic regression model performed better in the current dataset, but this may not always be the case.