You may have come across these terms before Bias and Variance but precisely what is Bias and Variance? These terms might sound intimidating at first but believe me they are easier to grab and acts as fundamentals in your Machine Learning journey. In this article, we’ll break down what bias and variance are and understand their significance in the context of machine learning.
Table of Contents
What is Bias and Variance?
When the Machine Learning model determines the patterns in real-world problems and makes predictions on testing data, there is a deviation that occurs in those predictions, the difference between the actual values of the target data and the predicted values of the target data is known as bias error. It can lead to inaccurate predictions that are not what we want. Essentially, bias is the algorithm’s tendency to consistently learn the wrong thing by not taking into account all the information in the data. High bias can lead to the model underperforming and missing relevant relations between features while low bias can lead to overperforming of the model and capturing noise.
- Low Bias – In the Low Bias case the predicted values will be much closer to the actual values of the target variable, which generally means fewer assumptions are taken into account. This case is also called Overfitting.
- High Bias – In the High Bias, the predicted values will be far from the actual values of the target variable, which means more assumptions are made and the model wasn’t able to properly capture important patterns. This case is also called Underfitting.
Variance, on the other hand, is the model’s sensitivity to small fluctuations in the training data. It indicates how much the model’s predictions can vary for different training datasets. High variance can cause the model to overfit, capturing noise in the training data rather than the underlying patterns and vice versa. The model performance should not vary too much when getting fed with different subsets of the data, this indicates that the model is good at figuring out the patterns.
- Low Variance – Low Variance shows us that the model is less sensitive to the change in data and can give us consistent results without too much of fluctuations. This is a case of Underfitting
- High Variance – When High Variance occurs that means the fluctuation of the model’s performance is too much when shifting from one subset of the data frame to another. The model may perform well on some subset but poorly on another one, you can see this when your model performs well on the training set but when it’s time for the testing set, the performance significantly degrades, which clearly shows us that the new data can make our model go wild.
How to reduce High Bias in Machine Learning?
High Bias means your Model is Underfitting and it cannot take the more important features into consideration. To tackle this situation you can simply:
- Collect and Train on more data, as a lower amount of data can lead to high bias.
- Choose a more complex model because if the model is too simple it might not see the underlying important features. Consider using a more complex model architecture that can learn intricate relationships
- Decrease the Regularization of the model.
- Include more sophisticated features that can accurately describe the data.
How to reduce High Variance in Machine Learning?
High Variance clearly shows us that our Machine Learning Model is Overfitting and we need to make sure that it performs somehow consistently well on our subset datasets. We can:
- Reduce the complexity of our Machine Learning Model (we did the opposite in reducing high bias)
- Early Stopping can help in reducing the High Variance as you can monitor when the performance of the model is taking a hit and you can halt the training.
- Implement Cross Validation by which training and testing data can get split multiple times.
- Increase Regularization (we reduced the regularization term in reducing high bias)
Different Combinations of Bias and Variance
- Low-Bias, Low-Variance:
This is the sweet spot where low bias and low variance converge. The model captures the underlying patterns in the data while not getting overly swayed by outliers or noise. Achieving this ideal balance is the goal of every machine learning practitioner, but it is practically not possible.
- Low-Bias, High-Variance: Models with low bias and high variance have a tendency to closely follow the training data, even if it means capturing noise. These models might perform remarkably well on the training data but stumble when exposed to new, unseen data points.
- High-Bias, Low-Variance: When a model exhibits high bias and low variance, it tends to oversimplify the problem at hand. It’s like trying to fit a linear model to predict complex nonlinear relationships. Such models might struggle to capture intricate patterns, resulting in reduced accuracy.
- High-Bias, High-Variance:
With this, predictions are inconsistent and inaccurate. The model not only oversimplifies the problem but also struggles to generalize. This can happen when the chosen algorithm is too basic for the complexity of the data, leading to both systematic errors and erratic predictions.
The relationship between bias and variance is often described as a trade-off. We should aim for achieving a perfect balance between the two for building a robust and accurate Machine Learning model.
- High Bias, Low Variance: When a model has high bias and low variance, it means the model oversimplifies the problem and generalizes poorly to new, unseen data. It consistently misses relevant patterns and tends to underperform. This is often seen in models with too few parameters or overly strong regularization.
- Low Bias, High Variance: Conversely, a model with low bias and high variance is overly complex and fits the training data too closely. While it might perform well on the training data, it’s likely to perform poorly on new data due to its sensitivity to noise. This is a classic case of overfitting.
- Bias-Variance Sweet Spot: The goal is to find the sweet spot between bias and variance. It involves developing a model that captures the underlying patterns in the data without being overly sensitive to noise. Striking this balance results in a model that generalizes well to new data.