Machine Learning Introduction

Supervised Machine Learning

Unsupervised Machine Learning

Miscellaneous Topics in ML

Data Pre-Processing

There are four possible combinations of bias and variances, which are represented by the below diagram:

**1. Low-Bias, Low-Variance:**

The combination of low bias and low variance shows an ideal machine learning model. However, it is not possible practically.

**2. Low-Bias, High-Variance:**

With low bias and high variance, model predictions are inconsistent and accurate on average. This case occurs when the model learns with a large number of parameters and hence leads to an **overfitting**

**3. High-Bias, Low-Variance:**

With High bias and low variance, predictions are consistent but inaccurate on average. This case occurs when a model does not learn well with the training dataset or uses few numbers of the parameter. It leads to **underfitting** problems in the model.

**4. High-Bias, High-Variance:**

With high bias and high variance, predictions are inconsistent and also inaccurate on average.

High variance can be identified if the model has:

• Low training error and high test error.

High Bias can be identified if the model has:

• High training error and the test error is almost similar to training error.

While building the machine learning model, it is really important to take care of bias and variance in order to avoid overfitting and underfitting in the model. If the model is very simple with fewer parameters, it may have low variance and high bias. Whereas, if the model has a large number of parameters, it will have high variance and low bias. So, it is required to make a balance between bias and variance errors, and this balance between the bias error and variance error is known as **the Bias-Variance trade-off.**

For an accurate prediction of the model, algorithms need a low variance and low bias. But this is not possible because bias and variance are related to each other:

Bias-Variance trade-off is a central issue in supervised learning. Ideally, we need a model that accurately captures the regularities in training data and simultaneously generalizes well with the unseen dataset. Unfortunately, doing this is not possible simultaneously. Because a high variance algorithm may perform well with training data, but it may lead to overfitting to noisy data. Whereas, high bias algorithm generates a much simple model that may not even capture important regularities in the data. So, we need to find a sweet spot between bias and variance to make an optimal model.

Hence, the *Bias-Variance trade-off is about finding the sweet spot to make a balance between bias and variance errors.*

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