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Difference between Bias and Variance in Machine Learning
Algorithms are ubiquitous, and a good number of us make use of them, although we may not even be aware that one is involved in the process. We require an algorithm to use a computer to solve a problem. When it comes to transforming datasets into models, machine learning relies on a number of different techniques.
Both bias and variance are essential components to understand when working with machine learning. When it comes to achieving high levels of accuracy in any machine learning algorithm, having a solid understanding of each of these concepts is essential.
What is Bias in Machine Learning?
Every machine learning algorithm has a prediction error, which can be segmented into three subcomponents: bias error, variance error, and irreducible error. In the process of machine learning, faulty assumptions can lead to the occurrence of a phenomena known as bias.
Bias can emerge in the model of machine learning. When an algorithm generates results that are systematically prejudiced due to some inaccurate assumptions that were made throughout the process of machine learning, this is an example of bias.
Bias is analogous to a systematic error. They are presumptions that are made by a model in order to simplify the process of learning the target function.
A high bias indicates that both the error in the training data and the error in the testing data are greater. To prevent the issue of underfitting, it is usually advised that an algorithm have a minimal bias in order to maximize accuracy.
Let's imagine you've chosen a model that is incapable of deriving even the fundamental patterns from the data set; this is what we mean when we talk about underfitting. When you apply an algorithm to a problem and find that it does not fit adequately, you have a situation that might be characterized as biased.
A high bias model has the following characteristics
- Failure to gather proper data trends.
- Possibility of having an improper fit.
- More generic and simplistic in an excessive degree.
- A high frequency of errors.
What is Variance in Machine Learning?
The difference in the accuracy of a machine learning model's predictions between the training data and the test data is referred to as variance. A variance error is what we call the situation when a change in the performance of the model is brought about by a variation in the dataset.
Variance refers to the magnitude of the change that would occur in the estimation of the target function if a different set of training data was utilized. Because a machine learning algorithm infers the target function from the training data, it is reasonable to anticipate that the method will exhibit some degree of variability.
Variance is dependent on a single training set, and it is the factor that determines the inconsistency of the predictions made using various training sets.
When the variance is low, it suggests that the estimate of the target function will change only slightly when the training dataset is altered.
When the variance is high, it suggests that the estimate of the target function will change significantly when the training dataset is altered.
The particulars of the training data have a significant impact on the performance of machine learning algorithms that have a high variation.
A high variance model has the following characteristics −
- The presence of noise in the data set
- There is a possibility of overfitting.
- Complex models.
- Making an effort to bring all of the data points as close together as possible.
Difference between Bias and Variance in Machine Learning
The following table highlights the major differences between Bias and Variance in Machine Learning −
| Basis of comparison | Bias | Variance |
|---|---|---|
| Definition | When an algorithm is employed in a machine learning model and it does not fit well, a phenomenon known as bias can develop. Bias arises in several situations. | The term "variance" refers to the degree of change that may be expected in the estimation of the target function as a result of using multiple sets of training data. |
| Values | The disparity between the values that were predicted and the values that were actually observed is referred to as bias. | A random variable's variance is a measure of how much it varies from the value that was predicted for it. |
| Data | The model is incapable of locating patterns in the dataset that it was trained on, and it produces inaccurate results for both seen and unseen data. | The model recognizes the majority of the dataset's patterns and can even learn from the noise or data that isn't vital to its operation. |
Conclusion
Whatever model you use, you want to make sure that it strikes a good balance between the amount of bias and the amount of variance.
Any supervised machine learning algorithm should strive to achieve low bias and low variance as its primary objectives. This scenario, however, is not feasible for two reasons: first, bias and variance are negatively related to one another; and second, it is extremely unlikely that a machine learning model could have both a low bias and a low variance at the same time.
In contrast to bias, variance describes the situation in which the model accounts for the variations in the data as well as the noise. If you try to change the algorithm so that it is more suitable for a certain dataset, it may end up having a low bias, but the variance will increase.
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