Mean squared error (MSE) or L2 loss is the mean squared distance between the target's real values and the model's output. It is calculated as follows:
Mean squared error
by Konstantinos G. Margaritis, George Kyriakides
Hands-On Ensemble Learning with Python
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Section 1: Introduction and Required Software Tools
- A Machine Learning Refresher
- Getting Started with Ensemble Learning
- Section 2: Non-Generative Methods
- Voting
- Stacking
- Section 3: Generative Methods
- Bagging
- Boosting
- Random Forests
- Section 4: Clustering
- Clustering
- Section 5: Real World Applications
- Classifying Fraudulent Transactions
- Predicting Bitcoin Prices
- Evaluating Sentiment on Twitter
- Recommending Movies with Keras
- Clustering World Happiness
- Another Book You May Enjoy
Mean squared error
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