Choosing the wrong hyperparameters can really hurt how well your model works. Here’s why:
Underfitting: If your model is too simple, it won't see important patterns in the data. This happens when there is too much regularization or when the model's size is too small.
Overfitting: On the other hand, if your model is too complex, it starts to learn random noise instead of the real data. This usually happens when there is too little regularization.
Training Time: Some hyperparameters, like the learning rate or how big the batches of data are, can change how long it takes to train your model a lot.
Finding the right mix of these settings is very important. It takes some trial and error, but it’s all about experimenting and fine-tuning!
Choosing the wrong hyperparameters can really hurt how well your model works. Here’s why:
Underfitting: If your model is too simple, it won't see important patterns in the data. This happens when there is too much regularization or when the model's size is too small.
Overfitting: On the other hand, if your model is too complex, it starts to learn random noise instead of the real data. This usually happens when there is too little regularization.
Training Time: Some hyperparameters, like the learning rate or how big the batches of data are, can change how long it takes to train your model a lot.
Finding the right mix of these settings is very important. It takes some trial and error, but it’s all about experimenting and fine-tuning!