There is quite a bit of an overlap in the packages used for traditional analytics and machine learning. Unlike traditional analytics which only focuses on gaining insights from historical data, machine learning focuses more learning patterns from the same data that help in prediction. Every business domain makes use of Machine Learning and has a wide range of applications which includes fraud detection, consumer segmentation, optimization, sentiment analysis, recommender systems and a lot more.
Given the nature of data in recent times that includes noisy, unstructured data, machine learning algorithms help in processing information at the granular level. For example, a traditional linear model may be able to look at only a handful of components due to human intervention required in the model building.
The algorithms used in Machine Learning are mostly black box which is used to give inputs followed by the prediction of the result as an output. It does not require a human being to process data. Machine learning models may build a model on training dataset and score customers in a validation/ test dataset. The matter of fact is that the scalability of Machine Learning is much more efficient.
Machine Learning algorithms are also proven to give highly accurate results in all the steps that involve model building including outlier detection, missing values treatment, data mining, and predictions. This is because patterns in the data are observed at a much granular level that was traditionally possible. Machine learning can prove to be especially helpful while abstracting predictions from big data. Organizations like Google invests a considerable amount of resources to fast-track Machine Learning Research and make technology more effective.
The caret package stands for Classification and Regression Training. The grid search method of the carat R package searches parameters by combining various methods to estimate the performance of a given model. After looking at all the trial combinations, the grid search method finds a combination that gives the best results. This is one of the best packages used for machine learning, as it includes a variety of tools for developing predictive models. This package also contains tools for data splitting, pre-processing, feature selection, model tuning and variable importance estimation.
(Rpart) package in R language is used to build classification or regression models using a two-stage procedure and the resultant model is represented in the form of binary trees. To predict for a new dataset, you can use the predict function, which will give you the predicted classes.
Random Forest is one of the most widely used packages for machine learning. It can be used for regression and classification tasks. It can also be used for treating missing values and outliers.
This package is widely used for performing machine learning algorithms using fuzzy Clustering, Support vector machines, Naïve Bayes classifier.
This package is widely used for performing Multinomial Log-linear models and Neural networks.