strengthsweaknessesand parameters the main parameter of linear models is the regularization parametercalled alpha in the regression models and in linearsvc and logisticregression large values for alpha or small values for mean simple models in particular for the regression modelstuning these parameters is quite important usually and alpha are searched for on logarithmic scale the other decision you have to make is whether you want to use regularization or regularization if you assume that only few of your features are actually importantyou should use otherwiseyou should default to can also be useful if interpretability of the model is important as will use only few featuresit is easier to explain which features are important to the modeland what the effects of these features are linear models are very fast to trainand also fast to predict they scale to very large datasets and work well with sparse data if your data consists of hundreds of thousands or millions of samplesyou might want to investigate using the solver='sagoption in logisticregression and ridgewhich can be faster than the default on large datasets other options are the sgdclassifier class and the sgdregressor classwhich implement even more scalable versions of the linear models described here another strength of linear models is that they make it relatively easy to understand how prediction is madeusing the formulas we saw earlier for regression and classification unfortunatelyit is often not entirely clear why coefficients are the way they are this is particularly true if your dataset has highly correlated featuresin these casesthe coefficients might be hard to interpret supervised machine learning algorithms