R Tutorial: Supervised Learning in R: Regression
Key Takeaways
This video tutorial covers supervised learning in R, specifically regression, and introduces the concept of predicting numerical outcomes based on input variables. It uses R to demonstrate linear regression modeling and prediction.
Full Transcript
hi this is Nina Zuma and Sean Brown from Winn vector LLC welcome to our course on regression in machine learning regression is a task of predicting a numerical outcome based on the values of a set of inputs or independent variables in the statistical sense regression is predicting the expected value of an outcome but in the casual sense and for the purposes of this course will define regression as predicting numerical values this distinguishes regression from classification which is the task of making discrete predictions we're thinking how many units of a product will sell or how much of it was so our regression problems predicting if a customer will buy a product yes or no is a classification problem let's walk through an example regression task here we see a scatter plot of how fast a cricket chirps trips per second on the x-axis and the temperature on the y-axis let's say our goal is to predict the temperature by measuring cricket chirp rate suppose we fit a linear regression models at this data we'll show you how later in this course the predictions from this model are shown by the blue line if we assume that temperature is linearly related to cricket chirp rate then the blue line is the best fit line through the data and it predicts what the expected temperature should be based on the observed trip rate for example suppose we hear a cricket tripping at sixteen point five trips per second we see here that this model predicts a temperature of about eighty degrees let's get back and look at the reasons for modeling modeling from a scientific mindset focuses on understanding the process that produced the data how each variable affects the outcome modeling from an engineering or machine learning mindset focuses on predicting future events accurately and less on the relationship between variables and outcomes in this course will emphasize predicting accurately you'll learn several algorithms for fitting regression models by the end of the course you should have a better idea about the advantages and disadvantages of each algorithm now let's do an exercise to quickly review what you've learned
Original Description
Want to learn more? Take the full course at https://learn.datacamp.com/courses/supervised-learning-in-r-regression at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work.
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Hi. This is Nina Zumel and John Mount from Win-Vector LLC. Welcome to our course on regression in machine learning. Regression is the task of predicting a numerical outcome based on the values of a set of inputs, or independent variables.
In the statistical sense, regression is predicting the expected value of an outcome, but in the casual sense, and for the purposes of this course, we'll define regression as predicting numerical values.
This distinguishes regression from classification, which is the task of making discrete predictions.
Predicting how many units of a product will sell, or how much it will sell for, are regression problems.
Predicting if a customer will buy a product (yes or no) is a classification problem.
Let's walk through an example regression task.
Here we see a scatterplot of how fast a cricket chirps (chirps per second) on the x axis, and the temperature on the y axis. Let's say our goal is to predict the temperature by measuring cricket chirp rate. Suppose we fit a linear regression model to this data (we'll show you how later in this course). The predictions from this model are shown by the blue line. If we assume that temperature is linearly related to cricket chirp rate, then the blue line is the best fit line through the data, and it predicts what the expected temperature should be, based on the observed chirp rate.
For example, suppose we hear a cricket chirping at 16.5 chirps/second. We see here that this model predicts a temperature of about 80 degrees.
Let's step back and look at the reasons for modeling. Modeling from a scientific mindset focuses on understanding the process that produced the data: how each variable affects the outcome.
Modeling from an engineering or machine learning mindset focuses
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