Balancing Bias and Variance

The bias-variance trade-off is an important concept in machine learning and represents the tension between a model's ability to reduce training set errors and generalize to new examples.

Generally, as a model becomes more complex, such as by adding nodes to a decision tree, the bias of the model decreases. This is because the model is better able to adapt to the specific patterns and characteristics of the training set. However, this will also cause the model to lose a certain generalization ability, and the prediction results on the test set may become worse, that is, the variance of the model will increase.

Error conditions in the model

Errors in model predictions can be broken down into three parts:

The noise in the data itself is caused by a variety of reasons, such as physical equipment. Internal noise or human error. This inherent noise affects the accuracy of our measurements and database inputs. To combat this, we can take steps such as accurately calibrating equipment, training operators to reduce errors, and using data cleaning and processing techniques to eliminate the effects of noise.

2. The deviation of the model represents the difference between the model's prediction and the true label of the data.

3. The variance of the model indicates the changes in the model's predictions on different training sets.

Usually, we cannot control the internal noise of the model, we can only control the bias and variance of the prediction error. Since the prediction error for a given model is fixed, trying to reduce the bias will increase the variance and vice versa. This is the concept of the bias-variance trade-off.

Find the right balance

The ideal model will minimize bias and variance. However, in practice, the model cannot achieve both goals simultaneously.

When a model is too simple, such as using linear regression to fit a complex function, it ignores key information in the data set, resulting in high bias. Therefore, we call this situation the model underfitting the data.

When a model is too complex, such as using higher-order polynomials to model simple functions, it will fit to a specific training set and therefore have high variance. In this case, we say that the model overfits the data.

Therefore, when building and training a model, you should strive to find a model that is between overfitting and underfitting. There are several ways to find such models, depending on the specific machine learning algorithm used.

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