Variance relates to how the model evolves when different parts of the training data set are used. Simply put, variance is the variability in model prediction—how much the ML function can change based on the data set.
The bias term quantifies how well our model of choice, such as linear models, can represent the ground truth in the best-case situation. The variance term measures how closely trained models are grouped around this best-case scenario.
Bias can occur in machine learning when a model weights particular aspects or pieces of a dataset more heavily than others, or when training data does not accurately represent the intended use case, resulting in erroneous model output.
High Bias Low Variance: Models are consistent but generally wrong. High Bias, High Variance: On average, models are wrong and inconsistent. Low Bias Low Variance: On average, models are accurate and consistent.
High Bias - High Variance: On average, predictions are inconsistent and wrong. It is an ideal model since it has a low bias and a low variance. But we won't be able to do it. Predictions are inconsistent and correct on average due to low bias and high variance (overfitting). This can occur when the model contains a huge number of parameters.
where is the finaldata.csv
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