Lack of quality data:
One of the most important challenges in machine learning is obtaining and cleaning the data. The quality of the data has a big impact on the performance of the model.
Overfitting:
Overfitting refers to a model that performs well on the training data but poorly on unseen data. It occurs when a model is excessively complex, such as having too many parameters relative to the number of training examples.
Underfitting and Overfitting:
Underfitting refers to a model that is too simple to learn the underlying structure of the data. It occurs when a model is not able to capture the relevant patterns in the training data.
Choosing the right algorithm:
There are many machine learning algorithms to choose from, and each has its own strengths and weaknesses. Choosing the right algorithm for the task at hand is crucial.
Hyperparameter tuning:
Most machine learning algorithms have a number of hyperparameters that need to be set before training. Finding the right combination of hyperparameters can be difficult and time-consuming.
Managing the bias-variance tradeoff:
The bias-variance tradeoff is the balance between the complexity of the model and the amount of training data. A model with high bias will be oversimplified and may underfit the training data. A model with high variance will be too sensitive to the specific details of the training data and may overfit the training data.
