Improving machine learning models unit 5.pptx

Improving machine learning models

1. Machine Learning Unit – 5 Improving Machine Learning Models

2. Improving Machine Learning Models:- Businesses today depend on machine learning to optimize and scale their operations. When using this analytical tool, you’ll be able to generate essential data-driven insights. Creating a high-performance machine learning (ML) model is quite challenging.

3. Continued…… It’s even more challenging to boost the performance of a machine learning model to produce reliable and correct results. Data scientists acknowledge this as they often face a hard time testing a model’s performance to increase its accuracy.  

4. Continued…… If your ML model is struggling to deliver accurate and reliable results, here are ten effective ways to boost its performance. 1. Studying Learning Curves 2. Using Cross- Validation Correctly 3. Choosing the Right Error or Score Metric 4. Searching for the best Hyper-Parameters 5. Testing Multiple Models

5. Continued…… 6. Averaging Models 7. Staking Models 8. Applying Feature Engineering 9. Selecting Features and Examples 10. Looking for More Data

6. Studying learning curves As a first step to improving your results, you need to determine the problems with your model. Learning curves require you to verify against a test set as you vary the number of training instances.

7. Continued…… You’ll immediately notice whether you find much difference between your in-sample and out-of-sample errors. A wide initial difference is a sign of estimate variance; conversely, having errors that are both high and similar is a sign that you’re working with a biased model.

8. Continued……. A learning model of a Machine Learning model shows how the error in the prediction of a Machine Learning model changes as the size of the training set increases or decreases . Before we continue, we must first understand what variance and bias mean in the Machine Learning model.

9. Bias : It is basically nothing but the difference between the average prediction of a model and the correct value of the prediction. Models with high bias make a lot of assumptions about the training data. This leads to over-simplification of the model and may cause a high error on both the training and testing sets. However , this also makes the model faster to learn and easy to understand. Generally, linear model algorithms like Linear Regression have a high bias.