What Is Machine Learning?

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Из курса от партнера Wesleyan University

Machine Learning for Data Analysis

213 оценки

Wesleyan University

Machine Learning for Data Analysis

213 оценки

Курс 4 из 5 — Specialization Data Analysis and Interpretation

Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering. By completing this course, you will learn how to apply, test, and interpret machine learning algorithms as alternative methods for addressing your research questions.

Из урока

Decision Trees

In this session, you will learn about decision trees, a type of data mining algorithm that can select from among a large number of variables those and their interactions that are most important in predicting the target or response variable to be explained. Decision trees create segmentations or subgroups in the data, by applying a series of simple rules or criteria over and over again, which choose variable constellations that best predict the target variable.

What Is Machine Learning?2:03

Познакомьтесь с преподавателями

Jen Rose
Research Professor
Psychology
Lisa Dierker
Professor
Psychology

Machine learning encompasses a wide range of statistical methods, including linear

and logistic regression, which we learned about earlier in the specialization.

These methods can be used to describe associations, search for

patterns in a dataset, or make predictions about outcomes,

also called target or response variables, from a set of explanatory variables.

Also called predictors, features, or inputs.

Pattern detection, or describing associations among variables without

a specific outcome variable, is referred to as unsupervised learning.

When the goal is prediction of the value of a response variable based on a number

of predictors, then we are using a supervised learning approach.

Unlike the hypothesis testing approach you've been describing in the previous

courses in the specialization, we typically do not go into a machine

learning application with specific hypotheses in mind.

Instead we learn from the data.

We use a subset of observations from our dataset, which we call the training set,

to learn about the data.

And then test the statistical model we get from the training data set

on a different set of observations which we call the test set.

We split the data this way because the model that is fit using a machine learning

approach is going to fit best in the data set on which it was developed.

But it might not perform as well when we try to test it on a different set of

observations.

If the model doesn't work for a different data set then it's not of much use

even if it fits well in the training data set.

When we apply our statistical model from the training data set to the test data

set, we're interested primarily in the accuracy of our statistical model.

Accuracy can be assessed by what is called the test error rate, which is a measure of

the extent to which a model correctly classifies observations into categories.

Or correctly estimates the value of a different variable of interest for

each observation in the test data set.

The goal then is to identify a model that minimizes the test error rate.

That is, a model that accurately reflects true population associations or patterns.

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