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What is the meaning of bias in the ML world?
Machine learning bias, also sometimes called algorithm bias or AI bias, is a phenomenon that occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning process.
What is biased in machine learning?
Data bias in machine learning is a type of error in which certain elements of a dataset are more heavily weighted and/or represented than others. A biased dataset does not accurately represent a model’s use case, resulting in skewed outcomes, low accuracy levels, and analytical errors.
What is the difference between bias and variance in machine learning?
Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data.
What is overfitting and Underfitting?
Overfitting: Good performance on the training data, poor generliazation to other data. Underfitting: Poor performance on the training data and poor generalization to other data.
What does bias mean in a ML model?
Bias: It happens to be the tendency of an ML model to consistently learn the wrong relations by not taking in account all the features given for the training. A ML model with high bias won’t be able to learn relations between features effectively and hence would Underfit on the dataset leading to low accuracy while predicting.
What do you mean by bias in machine learning?
In the simplest terms, Bias is the difference between the Predicted Value and the Expected Value. To explain further, the model makes certain assumptions when it trains on the data provided. When it is introduced to the testing/validation data, these assumptions may not always be correct.
Where does bias and variance come into the picture?
This is where Bias and Variance come into the picture. What is Bias? In the simplest terms, Bias is the difference between the Predicted Value and the Expected Value. To explain further, the model makes certain assumptions when it trains on the data provided.
What happens when ML model has high variance?
An ML model with High variance causes the it to become highly flexible with respect to the data points of the dataset. Such a condition causes a model to Overfit on the training data leading to low accuracy while predicting.