biasthis is the error that arises due to the model (learning algorithmmaking wrong assumptions on the parameters in the underlying data the bias error is the difference between the expected or predicted value of the model estimator and the true or actual value which we are trying to predict if you remembermodel building is an iterative process if you imagine building model multiple times over dataset every time you get some new observationsdue to the underlying noise and randomness in the datapredictions will not always be what is expected and bias tries to measure the difference\error in actual and predicted values it can also be specified as the average approximation error that the models have over all possible training datasets the last part hereall possible training datasetsneeds some explanation the dataset that we observe and develop our models on is one of the possible combinations of data that exist all the possible combinations of each of the attributes\features that we have in our data will give rise to different dataset for exampleconsider if we have dataset with binary (categoricalfeaturesthen the size of that entire dataset would be data points the dataset that we model on will obviously be subset of this huge data so bias is the average approximation error that we can expect over subset of this entire dataset bias is mostly affected by our assumptions (or the model' assumptionsabout the underlying data and patterns for exampleconsider simple linear regression modelit makes the assumption that the dependent variable is linearly dependent on the independent variable whereas consider the case of decision tree model it makes no such assumption about the structure of the data and purely learns patterns from the data hencein relative sensea linear model may tend to have higher bias than decision tree model high bias makes model miss relevant relationships between features and the output variables in data variancethis error arises due to model sensitivity to fluctuations in the dataset that can arise due to new data pointsfeaturesrandomnessnoiseand so on it is the variance of our approximation function over all possible datasets it represents the sensitivity of the model prediction results on particular set of data points suppose you could have learned the model on different subset of all possible datasets then variance would quantify how the results of the model change with the change in the dataset if the results stay quite stable then the model would be said to having low variance but if the results vary considerably each time then the model would said to be having high variance consider the same example of contrasting linear model against decision tree modelunder the assumption that clear linear relationship exists between the dependent and the independent data variables then for sufficiently large dataset our linear model will always capture that relationship whereas the capability of decision tree model depends on the datasetif we get dataset which consists of lot of outlierswe are likely to get bad decision tree model hence we can make statement that the decision tree model will be having higher variance than linear regression model based on data and the underlying noise\randomness high variance makes model too sensitive to outliers or random noise instead of generalizing well an effective way to get clearer idea at this somewhat confusing concept is through visual representation of bias and varianceas depicted in figure -