How to Improve F-score of Machine Learning Models

The F-score of a machine learning model balances precision (how many of the predicted positives are actually positive) and recall (how many actual positives are correctly predicted). It is a metric used to evaluate the performance of a machine learning model in classification.

After building a model using Aidy, the F-score and related metrics will be displayed. A higher F-score (maximum of 100%) indicates a better model. If the F-score is below 50%, the model is considered poor and is not recommended for prediction tasks.

Below is a guide to improving the F-score of machine learning models:

1. Data Quality

• Data Cleanliness: Ensure the dataset is clean, with minimal issues such as typos, inconsistent data types, or missing values.
• Outlier Detection: Identify and manage outliers that could skew results. Aidy’s histogram visualizations can help detect irregularities after data import. 

2. Dataset Size

The size of your dataset significantly impacts model performance. While the ideal size depends on task complexity and model type, larger datasets generally lead to better accuracy and generalization—especially in deep learning.

If your model’s F-score is low, consider rebuilding it with a larger dataset to test for improvements.

3. Predictor/Feature Selection

Effective feature selection enhances model performance by reducing noise. Think of it like cooking: using the right ingredients (features) makes all the difference. Irrelevant features can spoil the outcome.

• Aidy provides model insights with predictor attributions. Focus on predictors with higher attribution scores.
• Consider adding new, relevant predictors and removing less important ones.
• Rebuild the model and compare the new F-score.

4. Overfitting

Overfitting occurs when a model performs well on training/testing data but poorly on new, unseen data. Simplifying the model by reducing the number of predictors can help prevent this.

• A common guideline is the “1 in 10 rule”: use no more than one predictor for every 10 data records.
• To assess overfitting, evaluate the model on a separate testing dataset. A significant drop in F-score suggests overfitting.

Reducing predictors may slightly lower performance, but it often improves generalization. Striking a balance between simplicity and performance is key—a more complex model isn’t always better if the F-score gain is marginal.

5. Feature Engineering

Transforming raw data into a more informative format can boost model performance.

• Use domain knowledge to engineer new predictors/features. For example, instead of using weight and height separately, calculate BMI to better represent health status. 
• In Aidy, data transformation or re-engineering can be done by point-and-click with no coding.

6. Cross-Validation

Robust validation ensures reliable F-score estimation.

• Don’t rely on a single test. Use multiple data splits to validate model performance across different subsets.
• Ensure class distributions are balanced across folds—especially important for imbalanced datasets.

7. Continuous Model Evaluation

Data evolves over time, so continuous evaluation is essential.

• Periodically re-evaluate your model using new data.
• If the F-score drops significantly, retrain and improve the model with updated data.
• Aidy automates and simplifies predictive analytics for you with no coding required.

By following the steps above, users can build more accurate, reliable machine learning models.

What if you’re not a data scientist? Can a layman use Machine Learning? Yes — absolutely, you can do all the above with the right tool like Aidy.