Build Simple Model for ML (Decision Tree)

Steps of building a model

[Refer to Kaggle: Your First Machine Learning Model
](https://www.kaggle.com/dansbecker/your-first-machine-learning-model)

The steps to building and using a model are:

• Define: What type of model will it be? A decision tree? Some other type of model? Some other parameters of the model type are specified too.
• Fit: Capture patterns from provided data. This is the heart of modeling.
• Predict: Just what it sounds like
• Evaluate: Determine how accurate the model’s predictions are.

Underfitting and Overfitting

Refer to Kaggle: Underfitting and Overfitting

• Overfitting: The model matches the training data almost perfectly, but does poorly in predicting new data.
• Underfitting: The model fails to capture important patterns & distinctions in the data, so it performs poorly even in training data.

As the Decision Tree Model's depth goes deeper, the Underfitting goes lesser, but there is "turn" at which it starts Overfitting and the error goes larger.

For finding out the “turning point”, we need to test out some depths , namely the max_leaf_nodes. At which the error start to turn descending to ascending, we will choose that depth as the best depth for training data in Decision Tree Model.

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