![]() ![]() ![]() High variance can lead to significant variations in predictions when the model is trained on different subsets of the training data. It tries to fit the training data so closely that it captures not only the true underlying patterns but also the random noise in the data. High Variance models : A model with high variance is overly sensitive to the noise or fluctuations in the training data. High bias models may miss capturing the true relationship between input features and the target variable, leading to inaccurate predictions both on the training and unseen data. High Bias models : A model with high bias simplifies the underlying patterns too much, often due to strong assumptions or constraints placed during model training. Low Bias models : A model with low bias has the ability to capture the true underlying patterns or relationships present in the data. Summary of Impact on Predictions: (TL DR) We would aim to avoid error due to bias, error to to variance, and error due to noise. The tradeoff between a model's ability to minimize bias and variance is foundational to training machine learning models. Prediction errors can be decomposed into two main subcomponents of interest: error from bias, and error from variance. ![]()
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