Madhuri Sarode : 24

Apache SparkMLIB

MLlib is Spark’s scalable machine learning library consisting of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as underlying optimization primitives.

MLlib is Spark’s machine learning (ML) library's goal is to make practical machine learning scalable and easy. At a high level, it provides tools such as:

• ML Algorithms: common learning algorithms such as classification, regression, clustering, and collaborative filtering
• Featurization: feature extraction, transformation, dimensionality reduction, and selection
• Pipelines: tools for constructing, evaluating, and tuning ML Pipelines
• Persistence: saving and load algorithms, models, and Pipelines
• Utilities: linear algebra, statistics, data handling, etc.

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Task 1 : Classification

A.Naïve Bayes

Naive Bayes is a simple multiclass classification algorithm with the assumption of independence between every pair of features. Naive Bayes can be trained very efficiently. Within a single pass to the training data, it computes the conditional probability distribution of each feature given label, and then it applies Bayes’ theorem to compute the conditional probability distribution of label given an observation and use it for prediction. NaiveBayes implements multinomial naive Bayes. It takes an RDD of LabeledPoint and an optional smoothing parameter lambda as input, an optional model type parameter (default is “multinomial”), and outputs a NaiveBayesModel, which can be used for evaluation and prediction. Bayes’ Theorem is stated as: P(h|d) = (P(d|h) * P(h)) / P(d)

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B.Decision Tree

Decision trees and their ensembles are popular methods for the machine learning tasks of classification and regression. Decision trees are widely used since they are easy to interpret, handle categorical features, extend to the multiclass classification setting, do not require feature scaling, and are able to capture non-linearities and feature interactions. Tree ensemble algorithms such as random forests and boosting are among the top performers for classification and regression tasks.

The decision tree is a greedy algorithm that performs a recursive binary partitioning of the feature space. The tree predicts the same label for each bottommost (leaf) partition. Each partition is chosen greedily by selecting the best split from a set of possible splits, in order to maximize the information gain at a tree node. In other words, the split chosen at each tree node is chosen from the set argmaxsIG(D,s) where IG(D,s) is the information gain when a split s is applied to a dataset D.

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3.Random Forest

Random forests are ensembles of decision trees. Random forests are one of the most successful machine learning models for classification and regression. They combine many decision trees in order to reduce the risk of overfitting. Like decision trees, random forests handle categorical features, extend to the multiclass classification setting, do not require feature scaling, and are able to capture non-linearities and feature interactions.

Basic algorithm Random forests train a set of decision trees separately, so the training can be done in parallel. The algorithm injects randomness into the training process so that each decision tree is a bit different. Combining the predictions from each tree reduces the variance of the predictions, improving the performance on test data.

Training The randomness injected into the training process includes:

Subsampling the original dataset on each iteration to get a different training set (a.k.a. bootstrapping). Considering different random subsets of features to split on at each tree node. Apart from these randomizations, decision tree training is done in the same way as for individual decision trees.

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Clustering

A.k-means clustering

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B.Logistic Regression

Logistic regression is a popular method to predict a categorical response. It is a special case of Generalized Linear models that predicts the probability of the outcomes. In spark.ml logistic regression can be used to predict a binary outcome by using binomial logistic regression, or it can be used to predict a multiclass outcome by using multinomial logistic regression. Use the family parameter to select between these two algorithms, or leave it unset and Spark will infer the correct variant.

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