Notes on machine learning (updated from time to time)

Learning flow

  1. Determining the content of implementation

  2. Obtain data

  3. Data preprocessing

  4. Method selection

  5. Hyperparameter selection

  6. Model learning

  7. Model evaluation → Go to 3, 4, 5

Reasons to separate training data and test data

・ At the time of system release All the data you have may be used as training data

・ If you want to evaluate accuracy Separate training data and test data, and evaluate test data with a model learned only from training data

Because the purpose of supervised learning is to predict unknown data

Underfitting and overfitting

Insufficient conformity

・ The prediction accuracy is low even for training data.

Overfitting

・ It fits well with the training data, but the prediction accuracy for the test data (unknown data) is low.

The most difficult part of machine learning is how to strike a good balance between underfitting and overfitting.

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