Data Science & Machine Learning

Quiz Explaination Supervised Learning: All data is labeled and the algorithms learn to predict the output from the input data Unsupervised Learning: All data is unlabeled and the algorithms learn to inherent structure from the input data. Semi-supervised Learning: Some data is labeled but most of it is unlabeled and a mixture of supervised and unsupervised techniques can be used to solve problem. Unsupervised learning problems can be further grouped into clustering and association problems. Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior. Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy A also tend to buy B.

What are the main parameters of the random forest model? max_depth: Longest Path between root node and the leaf min_sample_split: The minimum number of observations needed to split a given node max_leaf_nodes: Conditions the splitting of the tree and hence, limits the growth of the trees min_samples_leaf: minimum number of samples in the leaf node n_estimators: Number of trees max_sample: Fraction of original dataset given to any individual tree in the given model max_features: Limits the maximum number of features provided to trees in random forest model

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What are the main parameters in the gradient boosting model? There are many parameters, but below are a few key defaults. learning_rate=0.1 (shrinkage). n_estimators=100 (number of trees). max_depth=3. min_samples_split=2. min_samples_leaf=1. subsample=1.0.

How does L2 regularization look like in a linear model? L2 regularization adds a penalty term to our cost function which is equal to the sum of squares of models coefficients multiplied by a lambda hyperparameter. This technique makes sure that the coefficients are close to zero and is widely used in cases when we have a lot of features that might correlate with each other.

Which regularization techniques do you know? There are mainly two types of regularization, L1 Regularization (Lasso regularization) - Adds the sum of absolute values of the coefficients to the cost function. L2 Regularization (Ridge regularization) - Adds the sum of squares of coefficients to the cost function Here, Lambda determines the amount of regularization.

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What happens to our linear regression model if we have three columns in our data: x, y, z  —  and z is a sum of x and y? We would not be able to perform the regression. Because z is linearly dependent on x and y so when performing the regression would be a singular (not invertible) matrix.

What’s the difference between random forest and gradient boosting? Random Forests builds each tree independently while Gradient Boosting builds one tree at a time. Random Forests combine results at the end of the process (by averaging or "majority rules") while Gradient Boosting combines results along the way.

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What do we do with categorical variables? Categorical variables must be encoded before they can be used as features to train a machine learning model. There are various encoding techniques, including: One-hot encoding Label encoding Ordinal encoding Target encoding