If i am using all features of my dataset and i achieve 100% accuracy on my training set but 70% on my testing set, I would be correcting Model overfit
Training accuracy means that identical images are used for both training and testing, and test accuracy means that the trained model identifies independent images that have not been used in training.
Overfitting occurs when a model fails to generalize and instead fits the training dataset tightly. Overfitting occurs for several reasons, including: The training data size is too small and does not contain enough data samples to accurately represent all possible input data values.
The high level of accuracy measured on the training set is the result of overfitting. Overfitting occurs when a machine learning model tries to cover all data points in a given database or beyond the required data points.
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