Underfitting happens when a machine learning model is too simple to understand the patterns in its training data. This can make the model perform poorly, which is known as having high bias. Underfitting can show up in different ways:
High Training Error: Unlike overfitting, where the model does well with training data, an underfitted model usually has high error rates on both training data and new data. This means it struggles to learn important relationships.
Limited Flexibility: An underfitted model might use a basic approach or the wrong features. Because it ignores important details in the data, it can't adjust well to different situations.
Underfitting can hurt how useful a machine learning model is. For example, if a simple linear model tries to fit a complex, non-linear dataset, it will have a tough time. The model's predictions will often be wrong, showing that it can't even get things right with the training data. This points to a big gap between the model's understanding and what is really happening.
To fix underfitting, here are a few strategies:
Increase Model Complexity: Using more advanced algorithms can help the model learn better by allowing it to fit the data in more ways.
Feature Engineering: Adding relevant features or making changes to the existing ones can help the model pick up on important traits of the data.
In summary, it’s important to tackle underfitting. A good model needs to strike a balance between bias and variance to make accurate predictions.
Underfitting happens when a machine learning model is too simple to understand the patterns in its training data. This can make the model perform poorly, which is known as having high bias. Underfitting can show up in different ways:
High Training Error: Unlike overfitting, where the model does well with training data, an underfitted model usually has high error rates on both training data and new data. This means it struggles to learn important relationships.
Limited Flexibility: An underfitted model might use a basic approach or the wrong features. Because it ignores important details in the data, it can't adjust well to different situations.
Underfitting can hurt how useful a machine learning model is. For example, if a simple linear model tries to fit a complex, non-linear dataset, it will have a tough time. The model's predictions will often be wrong, showing that it can't even get things right with the training data. This points to a big gap between the model's understanding and what is really happening.
To fix underfitting, here are a few strategies:
Increase Model Complexity: Using more advanced algorithms can help the model learn better by allowing it to fit the data in more ways.
Feature Engineering: Adding relevant features or making changes to the existing ones can help the model pick up on important traits of the data.
In summary, it’s important to tackle underfitting. A good model needs to strike a balance between bias and variance to make accurate predictions.