Overfitting the Model
Overfitting is a general problem in data science when a model performs too well for training data. When it sees new data it will not perform that well. The overfitting model fails in the generalization of data. Generalization of the model is essential as it performs well for both training and unseen data. The overfitting model learns the training data well. It captures noise and random fluctuations rather than capturing underlying patterns. When a model trains too long on training data or when a model is too complex, it starts learning noise and other irrelevant information. The overfitted model cannot perform well on classification and prediction tasks. Low bias (error rates) and high variance are good indicators for the overfitting model.
Causes of using the overfitting model
Example: Prediction model: Let’s consider the price prediction of houses based on their square feet. We are using a polynomial regression model to capture the relationships between square feet and prices. The model is trained well so that it fits perfectly with the training data resulting in a low error rate. But when it’s used to predict with a new set of data it results in poor accuracy.
Key Aspects of overfitting the model
- Bias-Variance Trade-Off: Overfitting is also a part of the Bias-Variance trade-off. Complex models reduce bias but increase variance. The overfitting models have low bias and high variance leading to poor generalization.
- Regularization: Overfitting occurs when regularizations are not applied appropriately. We can use regularization methods like L1 and L2 regularization. It penalizes overly complex models and makes them generalized.
- Cross-Validation: Cross-validation techniques such as k-fold cross-validation will help in detecting and solving overfitting problems. It does it by evaluating the model on multiple subsets of data and provides a more robust method for generalization.
Practical Tips
- By implementing regularization techniques like L1 and L2 regularization to prevent overfitting.
- By using cross-validation methods such as k-fold cross-validation for robust model evaluation.
6 Common Mistakes to Avoid in Data Science Code
As we know Data Science is a powerful field that extracts meaningful insights from vast data. It is our job to discover hidden secrets from the available data. Well, that is what data science is. In this world, we use computers to solve problems and bring out hidden insights. When we enter into such a big journey, there are certain things we should watch out for. Those who like playing with data know the tricky part of understanding the data and the possibility of making mistakes during the data processing.
How can I avoid mistakes in my Data Science Code?
How can I write my Data Science code more efficiently?
To answer all your questions, In this article, you get to know Six common mistakes to avoid in data science code in detail.
Common MIstakes in Data Science
Table of Content
- Ignoring Data Cleaning
- Neglecting Exploratory Data Analysis
- Ignoring Feature Scaling
- Using default Hyperparameters
- Overfitting the Model
- Not documenting the code
- Conclusion
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