… A high variance indicates that the data points are very spread out from the mean, and from one another. A variance of zero indicates that all of the data values are identical. Variance measures how far a set of data is spread out. So we need to find the right/good balance without overfitting and underfitting the data. On the other hand if our model has large number of parameters then it's going to have high variance and low bias. If our model is too simple and has very few parameters then it may have high bias and low variance. How do you know that your model is high variance low bias? This can happen when the model uses a large number of parameters. … Low Bias - High Variance (Overfitting): Predictions are inconsistent and accurate on average. Low Bias - Low Variance: It is an ideal model. ![]() High Bias - High Variance: Predictions are inconsistent and inaccurate on average. A model with high variance may represent the data set accurately but could lead to overfitting to noisy or otherwise unrepresentative training data. ![]() A model that exhibits small variance and high bias will underfit the target, while a model with high variance and little bias will overfit the target.
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