Improving ML Model Performance

In the previous lesson, you saw ways to measure the performance of the machine learning model and set the expectations right. Now we move towards improving the performance.

Ideas for improving ML model performance

Here are a few general ideas to improve any ML model performance.

• Collecting more data
• Increase diversity in the training set
• Trying bigger/smaller network architecture
• Trying different technique of gradient descent
• Trying alternate network architecture

Assumptions in ML

When building models, we must keep a few assumptions in mind, including:

• Good performance on the training set
• Good performance on validation set
• Good performance on the test set
• Good performance on real data

To consider all these assumptions, we start with training data, apply some ideas, and moving to a validation set followed by the test data. If performance is not good on real-world data, we need to enhance our algorithm with large data in different sets and change the cost function according to the real problem.

Single number evaluation metric

While performing any machine learning task, we should try to make our target metric a single number. This will help when comparing models.

Consider the example of three classifiers: C1, C2, and C3. We have evaluated performance on the test set and these are the results

Now, one model is doing good on precision, and another model is doing good on recall. If we have ...