Bias :

Bias is a systematic error in a machine learning model. It is the difference between the average prediction of the model and the true value. Bias can be introduced into a model in a variety of ways, such as:

  • Underfitting: This occurs when the model is too simple and cannot capture the complexity of the data.
  • Overfitting: This occurs when the model is too complex and learns the training data too well, but is unable to generalize to new data.
  • Selection bias: This occurs when the training data is not representative of the real-world data that the model will be used on.
  • Confirmation bias: This occurs when the model is designed to confirm existing beliefs, rather than learn from the data.

à High bias and low bias are two different types of bias.

High bias: A model with high bias is unable to capture the underlying patterns in the data. It is too simple and makes too many assumptions. As a result, the model will make inaccurate predictions, even on the training data.

Here are some examples of High bias models:

·        Linear regression

·        Logistic regression

·        Naive Bayes

 

Low bias: A model with low bias is able to capture the underlying patterns in the data. It is complex enough to learn the training data well. However, if the model is too complex, it may overfit the training data and be unable to generalize to new data.

Here are some examples of Low bias models:

·        Decision trees

·        Support vector machines

·        Random forests

Ways to reduce high bias :

There are a number of ways to reduce high bias in machine learning models. Some of the most common methods include:

  • Use a more complex model: A more complex model will be able to learn more complex patterns in the data, which can help to reduce bias.
  • Increase the number of features: Adding more features to the model can also help to reduce bias, as it gives the model more information to learn from. However, it is important to carefully select the features that are added, as irrelevant features can increase the risk of overfitting.
  • Use a larger training dataset: A larger training dataset will give the model more examples to learn from, which can help to reduce bias. This is especially important if the training dataset is not representative of the real-world data that the model will be used on.
  • Use regularization techniques: Regularization techniques can help to prevent overfitting by penalizing the model for learning too complex of a relationship between the features and the target variable. Some common regularization techniques include L1 and L2 regularization.
  • Use ensemble methods: Ensemble methods combine the predictions of multiple models to produce a more accurate prediction. This can help to reduce bias by averaging out the biases of the individual models.