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Underfitting and Overfitting in Machine Learning
The document discusses overfitting and underfitting in machine learning, defining overfitting as a model that captures noise from the training data leading to high variance and low bias, and underfitting as a model that fails to capture the data's underlying trend, resulting in high bias and low variance. It outlines methods to avoid both issues, including cross-validation, regularization, and model complexity adjustments. Additionally, it emphasizes the importance of a balanced model that performs well in both training and testing phases.
In this document
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Introducing the concepts of overfitting and underfitting in machine learning.
Discusses overfitting, its characteristics, causes, performance issues, and techniques to avoid it.
Defines underfitting, its characteristics, reasons, performance issues, and strategies to avoid it.
Describes a good fit model that balances accuracy in both training and testing phases.
Presents methods to detect overfitting, underfitting, and a good fit model using training and test error.
Compares overfitting, underfitting, and good fit model characteristics clearly.
Illustrates overfitting and underfitting through an analogy involving students and exams.
Relates the previous examples to decision tree classifiers and the relevance of training/testing.
Provides references from various websites used to gather information for the presentation.
Closes the presentation with a thank you message.
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Underfitting and Overfitting in Machine Learning
- 1. Overfitting and Underfitting inMachine Learning
- 2. Overfitting in machinelearning Overfitting refers to a scenario when the model tries to cover all the data points present in the given dataset. The model starts caching noise and inaccurate values present in the dataset. Reduces the efficiency and accuracy of the model. The overfitted model has low bias and high variance
- 3. Overfitting in machinelearning Cont. Reasons for Overfitting
- 4. Overfitting in machinelearning Cont. Overfitted model performance The accuracy score is good and high during training but it decreases during testing. How to avoid Overfitting Using cross-validation Using Regularization techniques Implementing Ensembling techniques. Picking a less parameterized/complex model Training the model with sufficient data Removing features Early stopping the training
- 5. Underfitting in machinelearning Underfitting is just the opposite of overfitting. Underfitting occurs when our machine learning model is not able to capture the underlying trend of the data. An underfitted model has high bias and low variance.
- 6. Underfitting in machinelearning Cont. Reasons for Underfitting
- 7. Underfitting in machinelearning Cont. Underfitted model performance The accuracy score is low during training as well as testing. How to avoid Underfitting Preprocessing the data to reduce noise in data More training to the model Increasing the number of features in the dataset Increasing the model complexity Increasing the training time of the model to get better results.
- 8. Good fit modelin machine learning A good fit model is a balanced model, which is not suffering from underfitting and overfitting. This is a perfect model which gives good accuracy score during training and equally performs well during testing.
- 9. Detecting Overfitting AndUnderfitting And Good Fit Detecting for Classification and Regression: Error Overfitting Right Fit Underfitting Training Low Low High Test High Low High
- 10. Comparison between Overfittingand Underfitting VS. Good Fit in a Model
- 11. Example To UnderstandOverfitting vs. Underfitting (vs. Good Fitting) in Machine Learning Consider a AI class consisting of students and a professor
- 12. Example To Underfittingvs. Overfitting (vs. Best Fitting) in Machine Learning Cont. We can broadly divide the students into 3 features (Hobby, Interest, Attention).
- 13. Example To Underfittingvs. Overfitting (vs. Best Fitting) in Machine Learning Cont. The professor first delivers lectures and teaches the students about the problems and how to solve them. At the end of the day, the professor simply takes a quiz based on what he taught in the class.
- 14. Example To Underfittingvs. Overfitting (vs. Best Fitting) in Machine Learning Cont. So, let’s discuss what happens when the professor takes a classroom test at the end of the day: We can clearly infer that the student who simply memorizes everything is scoring better without much difficulty.
- 15. Example To Underfittingvs. Overfitting (vs. Best Fitting) in Machine Learning Cont. Now here’s the twist. Let’s also look at what happens during the semester final, when students have to face new unknown questions which are not taught in the class by the Professor.
- 16. How Does thisRelate to Underfitting and Overfitting in Machine Learning Summaries the students Class Test and Semester Exam scores with a feature of Interest. We Make a dataset with Class Test and Semester Exam scores as a training and testing dataset.
- 17. How Does thisRelate to Underfitting and Overfitting in Machine Learning This example relates to the problem dataset Which the train, test and validation scores of the dataset. This example relates to the problem Which we encountered during the train and test scores of the decision tree classifier.
- 18. How Does thisRelate to Underfitting and Overfitting in Machine Learning Cont. Let’s work on connecting this example with the results of the decision tree classifier.
- 19. References https://www.javatpoint.com/ https://www.shiksha.com/ https://www.analyticsvidhya.com/ https://www.baeldung.com/ https://www.geeksforgeeks.org/
- 20.