### How can underfitting be mitigated?

Underfitting can be mitigated using improved feature selection, choosing an appropriate algorithm, decreasing regularization

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Underfitting can be mitigated using improved feature selection, choosing an appropriate algorithm, decreasing regularization

A model that is underfit will produce evaluation metrics that are poor on the training data alone, such as high RMSE or misclassification rate.

The bias/variance tradeoff refers to the challenge of finding a model that both performs at a high level of accuracy on the data on which it is trained (bias) while at the same time generalizes well to unseen data (variance)

Underfitting occurs when a model fails to capture the complexity of the training data and thus is a poor representation of the relationship between the features and the target.

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- Machine Learning 101 (30)
- Statistics 101 (38)
- Supervised Learning (114)
- Regression (42)
- Classification (46)
- Logistic Regression (10)
- Support Vector Machine (10)
- Naive Bayes (4)
- Discriminant Analysis (5)
- Classification Evaluations (9)

- Classification & Regression Trees (CART) (23)

- Unsupervised Learning (55)
- Clustering (28)
- Distance Measures (9)
- Dimensionality Reduction (9)

- Deep Learning (23)
- Data Preparation (34)
- General (5)
- Standardization (6)
- Missing data (7)
- Textual Data (16)