Differentiate between high bias and high variance
Question
Differentiate between high bias and high variance on the following ground:-
-complexity of model
-performance on train and test dataset
-fit
This will clear your concept of bias and variance
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Answers ( 7 )
If the model is having high bais, then we are certainly letting go important information by under utilizing the features. The high bais is seen on less complex model and performs poorly on train and test data both.
However a high variance model is one where we tend to over fit the model, as in model understands the training data too well.. mostly seen in complex model with many features. And performs great on training data and works poorly on test data.
In machine learning, we thrive to achieve a model with low bias and low variance, various regularisation techniques are there to help achieve this.
High Bias – High Variance: Predictions are inconsistent and inaccurate on average. Low Bias – Low Variance: It is an ideal model. But, we cannot achieve this. Low Bias – High Variance (Overfitting)- Predictions are inconsistent and accurate on average. High bias is poor in train and test model and low, whereas high variance is good in training data but poor in test data
If the information provided on the sample data doesn’t represent the whole population then we get errors.
If very less info is provided by sample data this can lead us to more bias that is shift of focus towards one side. In terms of complexity, the more complex less bias and more variance, and vice versa.
W.r.t fit, over fitting happens there is large variance.and less bias
Underfitting causes high bias and less variance
They work well with trained data but with test data we get errors
Complexity of Model:
If model is too simple, it has high bias.
If model is highly complex it has high variance
Performance on train and test data set:
If model is performing poorly with training data set, then it has high bias.
If model is performing very well with training data set, but poorly with test data., then it has high variance
Fit
If model is underfit then it has high bias, if it is overfit then it has high variance .
High Bias and High Variance difference in terms of:
1.) complexity of model –
When the bias is high, the model needs to be made more complex by the addition of polynomial features and more input variables.
When the variance is high, the model needs to be made less complex as the data is overfitting. So, we need to reduce the number of input features, include more training data, and increase the regularization term.
2.) Performance on Train and Test Dataset-
A high bias value indicates a high training error and an almost similar amount of test error.
A high variance value indicates a low training error (due to overfitting) and high test error.
3.) Fitness:
A model with high biasedness and high variance is simply inconsistent and inaccurate as a whole.
A high variance, low bias lead to overfitting, and low variance, high bias lead to underfitting.
The error due to bias is taken as the difference between expected prediction of our model and the currect value which we are trying to predict.
High bias suggest more assumptions about the form of the target value.
High bias machine learning algorithm include linear regression,linear discriminate analysis and Logistic regression.When the bias is high, the model needs to be made more complex by the addition of polynomial features and more input variables.when model is high bias it is underfitting problem.
High variance suggest large changes to the estimate of the target function with changes to the training dataset.
High variance indicates that model is able to capture the underlying relationship very well,but it is incapable of producing similar performance on unseen (test) dataset generalization ability of the model is low.
A model with high biasedness and high variance is simply inconsistent and inaccurate as a whole.
A high variance, low bias lead to overfitting, and low variance, high bias lead to underfitting.
1.) complexity of model –
When the bias is high, the model needs to be made more complex by the addition of polynomial features and more input variables.
When the variance is high, the model needs to be made less complex as the data is overfitting. So, we need to reduce the number of input features, include more training data, and increase the regularization term.
2.) Performance on Train and Test Dataset-
A high bias value indicates a high training error and an almost similar amount of test error.
A high variance value indicates a low training error (due to overfitting) and high test error.
3.) Fitness:
A model with high biasedness and high variance is simply inconsistent and inaccurate as a whole.
A high variance, low bias lead to overfitting, and low variance, high bias lead to underfitting.