Machine Learning Flashcards
supervised
https://towardsdatascience.com/supervised-vs-unsupervised-learning-14f68e32ea8d
supervised learning is done using a ground truth, or in other words, we have prior knowledge of what the output values for our samples should be. the goal of supervised learning is to learn a function that, given a sample of data and desired outputs, best approximates the relationship between input and output observable in the data.
In both regression and classification, the goal is to find specific relationships or structure in the input data that allow us to effectively produce correct output data.
Common algorithms in supervised learning include logistic regression, naive bayes, support vector machines, artificial neural networks, and random forests.
unsupervised
The most common tasks within unsupervised learning are clustering, representation learning, and density estimation. In all of these cases, we wish to learn the inherent structure of our data without using explicitly-provided labels.
Unsupervised learning, on the other hand, does not have labeled outputs, so its goal is to infer the natural structure present within a set of data points.
Two common use-cases for unsupervised learning are exploratory analysis and dimensionality reduction.
For example, if an analyst were trying to segment consumers, unsupervised clustering methods would be a great starting point for their analysis. In situations where it is either impossible or impractical for a human to propose trends in the data, unsupervised learning can provide initial insights that can then be used to test individual hypotheses.
what are the best machine learning package in R
https://www.r-bloggers.com/what-are-the-best-machine-learning-packages-in-r/
As per CRAN there are around 8,341 packages that are currently available. Apart from CRAN, there are other repositories which contribute multiple packages. The simple straightforward syntax to install any of these machine learning packages is: install.packages (“Name_Of_R_Package”).
Few basic packages without which your life as a data scientist, will be tough include dplyr, ggplot2, reshape2 etc. In this article we will be more focused on packages used in the field of Machine Learning.
MICE rpart caret party random forest nnet e1071 kernlab
