The goal of any machine learning problem is to find a single model that will best predict our wanted outcome. Rather than making one model and hoping this model is the best/most accurate predictor we can make, ensemble methods take a myriad of models into account, and average those models to produce one final model. It is important to note that Decision Trees are not the only form of ensemble methods, just the most popular and relevant in DataScience today.
1. What is logistic regression? Logistic regression is a machine learning algorithm for classification. In this algorithm, the probabilities describing the possible outcomes of a single trial are modelled using a logistic function. 2. What is the syntax for logistic regression? Library: sklearn.linear_model.LogisticRegression Define model: lr = LogisticRegression() Fit model: model = lr.fit(x, y) Predictions: pred = model.predict_proba(test) 3. How do you split the data in train / test? Library: sklearn.model_selection.train_test_split Syntax: X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42) 4. What is decision tree? Given a data of attributes together with its classes, a decision tree produces a sequence of rules that can be used to classify the data. 5. What is the syntax for decision tree classifier? Library: sklearn.tree.DecisionTreeClassifier Define model: dtc = DecisionTreeClassifier() Fit model: model = dtc.fit(x, y) Predictions: p...

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