When working with machine learning. Achieving high levels of accuracy in any machine-learning algorithm requires a solid understanding of these components.
What is Bias in Machine Learning?
In machine learning, every algorithm has a prediction error, which consists of three subcomponents: bias error, variance error, and irreducible error. Faulty assumptions during the learning process can lead to bias.
Bias can occur in the machine learning model when inaccurate assumptions result in systematically biased results.
A high-bias model exhibits the following characteristics:
- Fails to capture proper data trends.
- May have an improper fit.
- Tends to be overly generic and simplistic.
- Results in a high frequency of errors.
What is Variance in Machine Learning?
Variance refers to the difference in accuracy between a machine learning model’s predictions on the training data and the test data. Variance error occurs when changes in the dataset lead to variations in the model’s performance.
Variance represents the magnitude of change that would occur in estimating the target function if a different set of training data were used. Since machine learning algorithms learn from training data, some degree of variability is expected.
A high-variance model exhibits the following characteristics:
- Presence of noise in the dataset.
- Potential for overfitting.
- Utilization of complex models.
- Attempts to bring all data points as close together as possible.
Having a solid understanding of bias and variance helps in achieving better accuracy and performance in machine learning algorithms.
Difference between Bias and Variance in Machine Learning
The table below outlines the main differences between Bias and Variance in Machine Learning:
| Comparison Basis | Bias | Variance |
|---|---|---|
| Definition | Bias occurs when an algorithm in a machine learning model does not fit well, leading to inaccurate results. It can happen in various situations. | Variance refers to the degree of change that can be expected in the estimation of the target function when using different sets of training data. |
| Values | Bias represents the difference between the predicted values and the actual observed values. | Variance measures how much a random variable deviates from its predicted value. |
| Data | A biased model fails to identify patterns in the dataset it was trained on, resulting in inaccurate predictions for both known and unknown data. | A model with high variance recognizes most of the patterns in the dataset, including noise or non-essential data. |