. Differentiate between Training data and Testing Data

Training Data and Testing Data are two types of data sets used in Machine Learning. Here's how they differ:

1. Purpose:

   • Training Data: This is the data set on which the machine learning model is built and trained. It's used to identify patterns, relationships, or trends that can be learned and later applied to the testing data.
   • Testing Data: This data set is used to evaluate the performance and generalizability of the trained model. It's unseen data for the model and helps to understand how well the model can predict new, unseen data.

2. Size:

   • Training Data: Typically, a larger portion of the dataset is used for training. This could be anywhere from 60% to 80% of the total dataset.
   • Testing Data: The remaining portion of the dataset, usually between 20% to 40%, is used for testing.

3. Usage in Model Building:

   • Training Data: The model learns from this data. It's used to adjust the weights and biases in supervised learning, for instance.
   • Testing Data: This data is not used in the learning process. It's only used to evaluate the model's performance.

4. Impact on Model Accuracy:

   • Training Data: If the model fits this data too well, it may lead to overfitting, where the model performs well on the training data but poorly on unseen data.
   • Testing Data: If the model performs well on this data, it indicates that the model has good predictive power and can generalize well to unseen data.

In summary, training data is used to train the model, while testing data is used to validate the model's performance.