PySpark MLlibMachine Learning is a technique of data analysis that combines data with statistical tools to predict the output. This prediction is used by the various corporate industries to make a favorable decision. PySpark provides an API to work with the Machine learning called as mllib. PySpark's mllib supports various machine learning algorithms like classification, regression clustering, collaborative filtering, and dimensionality reduction as well as underlying optimization primitives. Various machine learning concepts are given below:
The pyspark.mllib library supports several classification methods such as binary classification, multiclass classification, and regression analysis. The object may belong to a different class. The objective of classification is to differentiate the data based on the information. Random Forest, Naive Bayes, Decision Tree are the most useful algorithms in classification.
Clustering is an unsupervised machine learning problem. It is used when you do not know how to classify the data; we require the algorithm to find patterns and classify the data accordingly. The popular clustering algorithms are the Kmeans clustering, Gaussian mixture model, Hierarchical clustering.
The fpm means frequent pattern matching, which is used for mining various items, itemsets, subsequences, or other substructure. It is mostly used in largescale datasets.
The mllib.linalg utilities are used for linear algebra.
It is used to define the relevant data for making a recommendation. It is capable of predicting future preference and recommending the top items. For example, Online entertainment platform Netflix has a huge collection of movies, and sometimes people face difficulty in selecting the favorite items. This is the field where the recommendation plays an important role.
The regression is used to find the relationship and dependencies between variables. It finds the correlation between each feature of data and predicts the future values. The mllib package supports many other algorithms, classes, and functions. Here we will understand the basic concept of pyspak.mllib. MLlib FeaturesThe PySpark mllib is useful for iterative algorithms. The features are the following:
Let's have a look at the essential libraries of PySpark MLlib. MLlib Linear RegressionLinear regression is used to find the relationship and dependencies between variables. Consider the following code: Output: +++++++++  _c0 _c1 _c2 _c3 _c4 _c5 _c6 _c7 +++++++++  Email Address AvatarAvg Session Length Time on App Time on WebsiteLength of MembershipYearly Amount Spent [email protected]835 Frank TunnelW... Violet 34.49726772511229 12.65565114916675 39.57766801952616 4.0826206329529615 587.9510539684005  [email protected]4547 Archer Commo... DarkGreen 31.9262720263601611.10946072868256437.268958868297744 2.66403418213262 392.2049334443264  [email protected]24645 Valerie Uni... Bisque33.00091475564267511.33027805777751237.110597442120856 4.104543202376424 487.54750486747207 [email protected]1414 David Throug... SaddleBrown 34.3055566297555413.717513665142507 36.72128267790313 3.120178782748092 581.8523440352177 [email protected]14023 Rodriguez P...MediumAquaMarine 33.3306725236463912.795188551078114 37.53665330059473 4.446308318351434 599.4060920457634 [email protected]645 Martha Park A... FloralWhite33.87103787934197612.026925339755056 34.47687762925054 5.493507201364199 637.102447915074 [email protected]68388 Reyes Light... DarkSlateBlue 32.0215955013870111.366348309710526 36.68377615286961 4.685017246570912 521.5721747578274  [email protected]Unit 6538 Box 898... Aqua32.739142938380326 12.35195897300293 37.37335885854755 4.4342734348999375 549.9041461052942 [email protected]860 Lee KeyWest D... Salmon 33.9877728956856413.38623527567643637.534497341555735 3.2734335777477144 570.2004089636196 +++++++++ only showing top 10 rows In the following code, we are importing the VectorAssembler library to create a new column Independent feature: Output: ++ Independent Feature ++ 34.49726772511229  31.92627202636016  33.000914755642675 34.30555662975554  33.33067252364639  33.871037879341976 32.02159550138701  32.739142938380326 33.98777289568564  ++ Output: ++++ Independent Feature  Yearly Amount Spent ++++ 34.49726772511229  587.9510539684005  31.92627202636016  392.2049334443264  33.000914755642675  487.5475048674720  34.30555662975554  581.8523440352177  33.33067252364639  599.4060920457634  33.871037879341976  637.102447915074  32.02159550138701  521.5721747578274  32.739142938380326  549.9041461052942  33.98777289568564  570.2004089636196  ++++ PySpark provides the LinearRegression() function to find the prediction of any given dataset. The syntax is given below: MLlib K Mean ClusterThe K Mean cluster algorithm is one of the most popular and commonly used algorithms. It is used to cluster the data points into a predefined number of clusters. The below example is showing the use of MLlib KMeans Cluster library: Parameters of PySpark MLlibThe few important parameters of PySpark MLlib are given below:
It is RDD of Ratings or (userID, productID, rating) tuple.
It represents Rank of the computed feature matrices (number of features).
It represents the number of iterations of ALS. (default: 5)
It is the Regularization parameter. (default : 0.01)
It is used to parallelize the computation of some number of blocks. Collaborative Filtering (mllib.recommendation)Collaborative filtering is a technique that is generally used for a recommender system. This technique is focused on filling the missing entries of a useritem. Association matrix spark.ml currently supports modelbased collaborative filtering. In collaborative filtering, users and products are described by a small set of hidden factors that can be used to predict missing entries. Scaling of the regularization parameterThe regularization parameter regParam is scaled to solve leastsquares problem. The leastsquare problem occurs when the number of ratings are usergenerated in updating user factors, or the number of ratings the product received in updating product factors. Coldstart strategyThe ALS Model (Alternative Least Square Model) is used for prediction while making a common prediction problem. The problem encountered when user or items in the test dataset occurred that may not be present during training the model. It can occur in the two scenarios which are given below:
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