Let's break it down step by step.Classification$1.$ Conceptual Questions Difference between supervised and unsupervised learning: $-$ Supervised learning involves training a model on a labeled dataset, meaning each training example is paired with an output label. Examples include classification and regression. $-$ Unsupervised learning deals with unlabeled data, aiming to infer the natural structure within a set of data points. Examples include clustering and dimensionality reduction. $-$ Classification belongs to supervised learning because it involves learning from labeled data to predict the category of new, unseen data points. Definitions in the context of classification models: $-$ True Positive $($TP$)$: The model correctly predicts the positive class. $-$ False Negative $($FN$)$: The model incorrectly predicts the negative class when it is actually positive. $-$ Precision: The ratio of true positives to the sum of true positives and false positives. It measures the accuracy of the positive predictions. Precision $=$ TP $/($TP $+$ False Positive$)-$ Recall: The ratio of true positives to the sum of true positives and false negatives. It measures the ability of the model to capture all positive instances. Recall $=$ TP $/($TP $+$ FN$)2.$ Coding$/$Practical Train a decision tree classifier in Python: $```$python from sklearn.datasets import load$\_$iris from sklearn.model$\_$selection import train$\_$test$\_$split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import confusion$\_$matrix, classification$\_$report # Load dataset data $=$ load$\_$iris$()$ X $=$ data.data y $=$ data.target # Split the dataset X$\_$train, X$\_$test, y$\_$train, y$\_$test $=$ train$\_$test$\_$split$($X$,$ y$,$ test$\_$size$=0\mathrm{.}3,$ random$\_$state$=42)$ # Train the model clf $=$ DecisionTreeClassifier$()$ clf$.$fit$($X$\_$train, y$\_$train$)$ # Predict y$\_$pred $=$ clf$.$predict$($X$\_$test$)$ # Evaluate the model print$($confusion$\_$matrix$($y$\_$test, y$\_$pred$))$ print$($classification$\_$report$($y$\_$test, y$\_$pred$))```$ This code loads the Iris dataset, splits it into training and testing sets, trains a decision tree classifier, makes predictions on the test set, and evaluates the model using a confusion matrix and classification report.Feel free to ask if you need further details or explanations!