How do I increase my F1 score in random forest?
How to improve F1 score for classification
- StandardScaler()
- GridSearchCV for Hyperparameter Tuning.
- Recursive Feature Elimination(for feature selection)
- SMOTE(the dataset is imbalanced so I used SMOTE to create new examples from existing examples)
How can I improve my F1 score with imbalanced data?
Use a better classification algorithm and better hyper-parameters. Over-sample the minority class, and/or under-sample the majority class to reduce the class imbalance. Use higher weights for the minority class, although I’ve found over-under sampling to be more effective than using weights.
How can the F1-score help with dealing with class imbalance?
F1 = 2 * (PRE * REC) / (PRE + REC) What we are trying to achieve with the F1-score metric is to find an equal balance between precision and recall, which is extremely useful in most scenarios when we are working with imbalanced datasets (i.e., a dataset with a non-uniform distribution of class labels).
What’s a good F1-score in ( not )?
You can also try boosting. I call imbalanced dataset is when you have a ratio of 90|10 at least. Your problem is not imbalanced. F1 score is not a Loss Function but a metric. In your GridsearchCV you are minimising another loss function and then selecting in your folds the best F1 metric.
What’s the difference between Balanced Accuracy and F1 score?
One major difference is that the F1-score does not care at all about how many negative examples you classified or how many negative examples are in the dataset at all; instead, the balanced accuracy metric gives half its weight to how many positives you labeled correctly and how many negatives you labeled correctly.
Is the F1 score a loss function or metric?
Your problem is not imbalanced. F1 score is not a Loss Function but a metric. In your GridsearchCV you are minimising another loss function and then selecting in your folds the best F1 metric. It is important to understand these concepts.