QUADRATIC MODEL AND OVERFITTING

QUADRATIC MODEL:A quadratic model is also known as quadratic equation or quadratic function,this model describes about the relation between a dependent variable and independent variable using quadratic polynomial equation.

the euqation for the quadratic model is: y=ax2+bx+c

where , a,b&c are constants,but a not equal to zero.

x is independent variable

y is dependent variable.

OVERFITTING:verfitting is a common problem in machine learning and statistical modeling, where a model learns the training data too well and captures noise or random fluctuations in the data rather than the underlying patterns or relationships.

Key characterstics of overfitting:

1. High Training Accuracy, Low Test Accuracy: An overfit model will perform extremely well on the training data, often achieving close to 100% accuracy or very low error. However, when tested on new data (validation or test set), its performance significantly degrades.
2. Excessive Complexity: Overfit models are often overly complex, with too many parameters or too much flexibility. They may have intricate decision boundaries or functions that try to fit every data point precisely.
3. Noise Capture: Overfitting models tend to capture the noise in the training data, which includes random variations or outliers that are not representative of the underlying patterns.

    

   Ways to mitigate overfitting:

   1. Simplify the Model: Reduce the complexity of the model by using fewer parameters or features. For example, in the case of deep neural networks, you can decrease the number of layers or neurons.
   2. Increase Training Data: Gathering more training data can help the model generalize better, as it has a larger sample to learn from.
   3. Cross-Validation: Use techniques like k-fold cross-validation to assess the model’s performance on multiple subsets of the data, which can provide a more robust estimate of its generalization performance.
   4. Regularization: Apply regularization techniques such as L1 or L2 regularization to penalize overly complex models and encourage simpler solutions.
   5. Feature Selection: Carefully choose and engineer relevant features, discarding those that do not contribute to the model’s predictive power.