A Classification Model: Understanding the Basics

A classification model is a type of supervised learning model that predicts categories or discrete categories, such as predicting if a picture is of a cat or a dog, or if a patient has a particular disease. This type of model is useful when there are only a small number of possible outputs. For example, in image recognition, the model might predict one of two classes: "cat" or "dog." In medical diagnosis, the model might predict one of ten possible medical conditions.

Understanding Classification vs. Regression

It's worth noting that classification and regression are two different types of machine learning problems. While classification involves predicting discrete categories, regression involves predicting continuous values. The key difference between the two is that in classification, there are only a small number of possible outputs, whereas in regression, there are infinitely many possible numbers that the model could output.

Visualizing Classification Data

To better understand classification data, it can be helpful to visualize it as a plot or as a data table. The plot shows the relationship between two variables: the size of the house (on the horizontal axis) and the price (on the vertical axis). Each point on the plot corresponds to one row in the data table, which includes a set of inputs (size of the house) and outputs (price).

Data Representation Notation

In machine learning, it's common to use notation to describe data. The standard notation for describing input variables is lowercase x, also known as the feature or input variable. For example, for the first training example with an input size of 2104 square feet, we would write x = 2104.

The output variable, which is also sometimes called the target variable, is denoted in lowercase y. For the same example, where the price of the house is $400,000, we would write y = 400.

Data Set Notation

A data set that is used to train a model is commonly referred to as a training set. The training set includes all the data points that the model will learn from in order to make predictions on new, unseen data. In our example, our client's house is not part of this training set because it has not yet been sold and therefore its price is unknown.

Notation for Training Examples

When referring to individual training examples, we use lowercase M (m) to denote the total number of training examples. For a single training example, we write the pair of numbers x, y (where x is the input variable and y is the output variable).

For instance, if we're looking at the first training example in our data set, which corresponds to row 1 in our table on the left, we would refer to it as X^1, comma Y^1. The superscript 1 indicates that this is the first training example, and it refers to the corresponding row in the table.

In conclusion, understanding classification models, their differences from regression models, and how to visualize and represent data are essential concepts in machine learning. By grasping these fundamental ideas, you can begin to explore more advanced topics in machine learning and start building your own predictive models.

"WEBVTTKind: captionsLanguage: enin this video we'll look at what the overall process of supervised learning is like specifically you see the first model of this course a linear regression model that just means filling a straight line to your data is probably the most widely used learning algorithm in the world today and as you get familiar with linear regression many of the concepts you see here will also apply to other machine learning models models that you see later in this specialization let's start with a problem that you can address using linear regression say you want to predict the price of a house based on the size of a house this is the example we seen earlier this week we're going to use the data set on house sizes and prices from Portland a city in the United States here with a graph where the horizontal axis is the size of the house in square feet and the vertical axis is the price of house in thousands of dollars let's go ahead and plot the data points for various houses in the data set here at each data points each of these little crosses is a house with a size and the price that most recently was sold for now let's say your real estate agent in Portland and you're helping a client sell her house and she's asking you how much do you think you're gonna get for this house this data set might help you estimate the price she could get for it you start by measuring the size of the house and it turns out that her house is 1250 square feet how much do you think this house could sell for one thing you could do is you can build a linear regression model from this data set your model will fit a straight line to the data which might look like this and based on this straight line fig to the data you can kind of see that if a holes is 1250 square feet it will intersect the best fit line over here and if you trace that to the vertical axis on the left you can see the price is maybe around here say about two hundred and twenty thousand dollars so this is an example of what's called a supervised learning model we call this supervised learning because you are first training your model by giving a data that's the right answers because you give the model examples of hoses with both the size of the holes as well as the price that the model should predict for each house where here the prices that is the right answers are given for every house in the data set this linear regression model is a particular type of supervised learning model is called a regression model because it predicts numbers as the output like prices in dollars any supervised learning model that predicts a number such as 220 000 or 1.5 or negative 33.2 is addressing what's called a regression problem so linear regression is one example of a regression model but there are other models for addressing regression problems too and we'll see some of those later in course two of this specialization and just remind you in contrast with the regression model the other most common type of supervised learning model is called a classification Model A classification model predicts categories or discrete categories such as predicting if a picture is of a cat meow or a dog woof or is given the medical record it has to predict if a patient has a particular disease you see more about classification models later in this course as well so as a reminder about the difference between classification and regression in classification there are only a small number of possible outputs if your model is recognizing cats versus dogs that's two possible outputs or maybe you're trying to recognize any of 10 possible medical conditions in a patient so there's a discrete finite set of possible outputs we call it a classification problem whereas in regression there are infinitely many possible numbers that the model could output in addition to visualizing this data as a plot here on the left there's one other way of looking at the data that would be useful and that's a data table here on the right the data comprises a set of inputs this would be the size of the house which is this column here it also has outputs you're trying to predict the price which is this column here notice that the horizontal and vertical axis correspond to these two columns the size and the price and so if you have say 47 rows in this data table then there are 47 of these lower crosses on the plot of the left each cross corresponding to one row of the table for example the first row of the table is a house with size 2104 square feet so that's around here and this house so for 400 000 which is around here so this first row of the table is plotted as this data point over here now let's look at some notation for describing the data this is notation that you find useful throughout your journey in machine learning as you increasingly get familiar with machine learning terminology this would be the terminology they can use to talk about machine learning concepts with others as well since a lot of this is quite standard across AI you'll be seeing this notation multiple times into specialization so it's okay if you don't remember everything to first sign through it will naturally become more familiar over time the data set that you just saw and that is used to train the model is called a training set note that your client's house is not in this data set because it's not yet sold so no one knows what this price is so to predict the price of your client's house you first train your model to learn from the training set and that model can then predict your clients houses price in machine learning the standard notation to denote the input here is lowercase x and we call this the input variable it is also called a feature or an input feature for example for the first house in your training set access the size of the holes so x equals 2104 the standard notation to denote the output variable which you're trying to predict which is also sometimes called the target variable is lowercase y and so here Y is the price of the house and for the first training example this is equal to 400 so y equals 400. so the data set has one row for each house and in this particular training set there are 47 rows with each row representing a different training example we're going to use lowercase M to refer to the total number of training examples and so here m is equal to 47 to indicate a single training example we're going to use the notation parentheses x comma y so for the first chain example X comma y this pair of numbers is 2104 comma 400. now we have a lot of different training examples we have 47 of them in fact so to refer to a specific training example this will correspond to a specific Row in this table on the left I'm going to use the notation X superscript in parentheses I comma Y superscript in parentheses I the superscript tells us that this is the if training example such as the first second or third up to the 47th training example I here refers to a specific Row in the table so for instance here is the first example when I equals 1 in the training set and so x super strip 1 is equal to 2104 and why superscript 1 is equal to 400 and let's add the superscript 1 here as well just a note this superscript I in parentheses is not exponentiation so when I write this this is not x squared this is not x to the power of 2 it just refers to the second training example so this I is just an index of the training set and refers to row I in the table in this video you saw what a training set is like as well as a standard notation for describing this training set in the next video let's look at what it'll take to take this training set you just saw and feed it to learning algorithm so that the algorithm can learn from this data let's see that in the next videoin this video we'll look at what the overall process of supervised learning is like specifically you see the first model of this course a linear regression model that just means filling a straight line to your data is probably the most widely used learning algorithm in the world today and as you get familiar with linear regression many of the concepts you see here will also apply to other machine learning models models that you see later in this specialization let's start with a problem that you can address using linear regression say you want to predict the price of a house based on the size of a house this is the example we seen earlier this week we're going to use the data set on house sizes and prices from Portland a city in the United States here with a graph where the horizontal axis is the size of the house in square feet and the vertical axis is the price of house in thousands of dollars let's go ahead and plot the data points for various houses in the data set here at each data points each of these little crosses is a house with a size and the price that most recently was sold for now let's say your real estate agent in Portland and you're helping a client sell her house and she's asking you how much do you think you're gonna get for this house this data set might help you estimate the price she could get for it you start by measuring the size of the house and it turns out that her house is 1250 square feet how much do you think this house could sell for one thing you could do is you can build a linear regression model from this data set your model will fit a straight line to the data which might look like this and based on this straight line fig to the data you can kind of see that if a holes is 1250 square feet it will intersect the best fit line over here and if you trace that to the vertical axis on the left you can see the price is maybe around here say about two hundred and twenty thousand dollars so this is an example of what's called a supervised learning model we call this supervised learning because you are first training your model by giving a data that's the right answers because you give the model examples of hoses with both the size of the holes as well as the price that the model should predict for each house where here the prices that is the right answers are given for every house in the data set this linear regression model is a particular type of supervised learning model is called a regression model because it predicts numbers as the output like prices in dollars any supervised learning model that predicts a number such as 220 000 or 1.5 or negative 33.2 is addressing what's called a regression problem so linear regression is one example of a regression model but there are other models for addressing regression problems too and we'll see some of those later in course two of this specialization and just remind you in contrast with the regression model the other most common type of supervised learning model is called a classification Model A classification model predicts categories or discrete categories such as predicting if a picture is of a cat meow or a dog woof or is given the medical record it has to predict if a patient has a particular disease you see more about classification models later in this course as well so as a reminder about the difference between classification and regression in classification there are only a small number of possible outputs if your model is recognizing cats versus dogs that's two possible outputs or maybe you're trying to recognize any of 10 possible medical conditions in a patient so there's a discrete finite set of possible outputs we call it a classification problem whereas in regression there are infinitely many possible numbers that the model could output in addition to visualizing this data as a plot here on the left there's one other way of looking at the data that would be useful and that's a data table here on the right the data comprises a set of inputs this would be the size of the house which is this column here it also has outputs you're trying to predict the price which is this column here notice that the horizontal and vertical axis correspond to these two columns the size and the price and so if you have say 47 rows in this data table then there are 47 of these lower crosses on the plot of the left each cross corresponding to one row of the table for example the first row of the table is a house with size 2104 square feet so that's around here and this house so for 400 000 which is around here so this first row of the table is plotted as this data point over here now let's look at some notation for describing the data this is notation that you find useful throughout your journey in machine learning as you increasingly get familiar with machine learning terminology this would be the terminology they can use to talk about machine learning concepts with others as well since a lot of this is quite standard across AI you'll be seeing this notation multiple times into specialization so it's okay if you don't remember everything to first sign through it will naturally become more familiar over time the data set that you just saw and that is used to train the model is called a training set note that your client's house is not in this data set because it's not yet sold so no one knows what this price is so to predict the price of your client's house you first train your model to learn from the training set and that model can then predict your clients houses price in machine learning the standard notation to denote the input here is lowercase x and we call this the input variable it is also called a feature or an input feature for example for the first house in your training set access the size of the holes so x equals 2104 the standard notation to denote the output variable which you're trying to predict which is also sometimes called the target variable is lowercase y and so here Y is the price of the house and for the first training example this is equal to 400 so y equals 400. so the data set has one row for each house and in this particular training set there are 47 rows with each row representing a different training example we're going to use lowercase M to refer to the total number of training examples and so here m is equal to 47 to indicate a single training example we're going to use the notation parentheses x comma y so for the first chain example X comma y this pair of numbers is 2104 comma 400. now we have a lot of different training examples we have 47 of them in fact so to refer to a specific training example this will correspond to a specific Row in this table on the left I'm going to use the notation X superscript in parentheses I comma Y superscript in parentheses I the superscript tells us that this is the if training example such as the first second or third up to the 47th training example I here refers to a specific Row in the table so for instance here is the first example when I equals 1 in the training set and so x super strip 1 is equal to 2104 and why superscript 1 is equal to 400 and let's add the superscript 1 here as well just a note this superscript I in parentheses is not exponentiation so when I write this this is not x squared this is not x to the power of 2 it just refers to the second training example so this I is just an index of the training set and refers to row I in the table in this video you saw what a training set is like as well as a standard notation for describing this training set in the next video let's look at what it'll take to take this training set you just saw and feed it to learning algorithm so that the algorithm can learn from this data let's see that in the next video\n"