Serving as the notes and reference for machine learning with R
Data Science and Machine Learning bootcamp with R
ISLR
● Machine learning is a method of data analysis that automates analytical model building.
● Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.
● Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known.
● For example, a piece of equipment could have data points labeled either “F” (failed) or “R” (runs).
● The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors.
● It then modifies the model accordingly.
● Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data.
● Supervised learning is commonly used in applications where historical data predicts likely future events.
● For example, it can anticipate when credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim.
● Or it can attempt to predict the price of a house based on different features for houses for which we have historical price data.
● Unsupervised learning is used against data that has no historical labels.
● The system is not told the "right answer." The algorithm must figure out what is being shown.
● The goal is to explore the data and find some structure within.
● Or it can find the main attributes that separate customer segments from each other.
● Popular techniques include self-organizing maps, nearest- neighbor mapping, k-means clustering and singular value decomposition.
● These algorithms are also used to segment text topics, recommend items and identify data outliers.
● Reinforcement learning is often used for robotics, gaming and navigation.
● With reinforcement learning, the algorithm discovers through trial and error which actions yield the greatest rewards.
● This type of learning has three primary components: the agent (the learner or decision maker), the environment (everything the agent interacts with) and actions (what the agent can do).
● The objective is for the agent to choose actions that maximize the expected reward over a given amount of time.
● The agent will reach the goal much faster by following a good policy.
● So the goal in reinforcement learning is to learn the best policy.