building or learning process usually these hyperparameters are tuned to get the optimal values as part of the model-tuning phase ( part of the learning phase itself another important point to remember is that the output model is generally dependent on the learning algorithm we choose for our data model types models can be differentiated on variety of categories and nomenclatures lot of this is based on the learning algorithm or method itselfwhich is used to build the model examples can be the model is linear or nonlinearwhat is the output of modelwhether it is parametric model or non-parametric modelwhether it is supervisedunsupervisedor semi-supervisedwhether it is an ensemble model or even deep learning based model refer to the section "machine learning methodsin to refresh your memory of possible machine learning methods used for building models on datasets in this sectionwe focus on some of the most popular models from supervised and unsupervised learning methods lassification models classification is one of the most readily recognizable machine learning tasks and it' covered in detail in it is subset of broader class of machine learning problems known as supervised learning supervised learning is the set of machine learning problems\tasks in which we have labeled dataset with input attributes and corresponding output labels or classes (discretethese inputs and corresponding outputs are then used in learning generalized systemwhich can be used to predict results (output class labelsfor previously unseen data points classification is one major part of the overall supervised learning domain the output of classification model is normally label or category to which the input data point may belong the task of solving classification (or in general any supervisedproblem involves training set of data in which we have the data points labeled with their correct classes/categories we then use supervised machine learning algorithms specific to classification problemsto generalize something similar to classification function for our problem the input to this classification function is exactly similar to the data that we used to train our model this input is typically data attributes or features that are generated in the feature engineering step typical classification models include the following major types of methodshoweverthe list is not exhaustive linear models like logistic regressionnaive bayesand support vector machines non-parametric models like -nearest neighbors tree based methods like decision trees ensemble methods like random forests (baggingand gradient boosted machines (boostingneural networks (mlps