The Importance of Numeric Features in Machine Learning: Feature Engineering Considerations

When working with numeric features, it's essential to consider several factors that can impact the performance of your machine learning model. One of the first questions you should ask yourself is whether the magnitude of the feature is its most important trait or just its direction. For instance, if you have a dataset containing restaurant health and safety ratings with the number of times a restaurant had major violations, you might care more about whether the restaurant had any major violations at all rather than the exact number of offenses.

In this scenario, we can recreate a new binary column representing whether or not a restaurant committed any violation. We start by creating a new column called "binary_violation" and setting it to zero. Then, we use the dot block notation to find all rows where the number of violations is greater than zero and set the "binary_violation" column to one. As you can see, all rows where the number of violations is equal to zero are also zeros in the "binary_violation" column, while all rows where the number of violations is greater than zero have a value of one.

This technique can be extended to group a numerical variable into more than two bins, which is often useful for variables like age or wage brackets where exact numbers are less relevant than the general magnitude of the value. For example, in a dataset containing restaurant health and safety ratings with the number of times a restaurant had major violations, we might want to create three groups: group one for restaurants with no offenses, group two for restaurants with one or two offenses, and group three for all restaurants with three or more offenses.

To achieve this, we can use the pandas cut function. We define the intervals using the bins argument, which in this case is a list of four values. We can also pass a list of labels to the bins argument, as shown here. Note that we want to include zero in the first bin, so we set the leftmost edge to be lower than zero. This results in all values between negative infinity and zero being labeled as 1, all values equal to 1 or 2 being labeled as 2, and all values greater than 2 being labeled as 3.

Binarizing and Binning Numeric Columns: Putting it into Practice

Now that you know how to binarize and bin numeric columns, it's time to put this into practice. When working with numeric features, there are several considerations to keep in mind. One of the most important questions is whether the magnitude of the feature is its most important trait or just its direction.

As mentioned earlier, if you have a dataset containing restaurant health and safety ratings with the number of times a restaurant had major violations, you might care more about whether the restaurant had any major violations at all rather than the exact number of offenses. In this scenario, we can recreate a new binary column representing whether or not a restaurant committed any violation.

We start by creating a new column called "binary_violation" and setting it to zero. Then, we use the dot block notation to find all rows where the number of violations is greater than zero and set the "binary_violation" column to one. As you can see, all rows where the number of violations is equal to zero are also zeros in the "binary_violation" column, while all rows where the number of violations is greater than zero have a value of one.

This technique can be extended to group a numerical variable into more than two bins, which is often useful for variables like age or wage brackets where exact numbers are less relevant than the general magnitude of the value. For example, in a dataset containing restaurant health and safety ratings with the number of times a restaurant had major violations, we might want to create three groups: group one for restaurants with no offenses, group two for restaurants with one or two offenses, and group three for all restaurants with three or more offenses.

To achieve this, we can use the pandas cut function. We define the intervals using the bins argument, which in this case is a list of four values. We can also pass a list of labels to the bins argument, as shown here. Note that we want to include zero in the first bin, so we set the leftmost edge to be lower than zero. This results in all values between negative infinity and zero being labeled as 1, all values equal to 1 or 2 being labeled as 2, and all values greater than 2 being labeled as 3.

In conclusion, when working with numeric features in machine learning, it's essential to consider the magnitude of the feature and its direction. Binarizing and binning numeric columns can be an effective way to simplify complex data and improve model performance. By understanding how to use these techniques, you can unlock the full potential of your numeric features and achieve better results with your machine learning models.

"WEBVTTKind: captionsLanguage: enas mentioned in the previous lesson most machine learning models require your data to be in numeric format however even if your raw data is all numeric there is still a lot you can do to improve your features numeric features can be used to represent a huge array of different characteristics and measurements pretty much anything that can be quantitatively measured can be recorded as numeric data for example age the price of an item counts and even spatial data such as coordinates depending on the use case numeric features can be treated in several different ways we will work through a few of the considerations and possible feature engineering steps to keep in mind when dealing with numeric data one of the first questions you should ask when working with numeric features is whether the magnitude of the feature is its most important trait or just its direction for example if you had a data set of restaurant health and safety railings containing the number of times a restaurant had major violations you might care far more about whether the restaurant had any major violations at all as you would rather not take any chances over whether it was a repeat offender looking at the Toei data set containing restaurant IDs and the number of times they had major violations we can see that some restaurants have no major violations but many have one or more we were be recreating a new binary column representing whether or not a restaurant committed any violation here we first create a new column binary violation and set it to zero then we use the dot block notation to find all rows where number of violations is greater than zero and set the binary violation column to one as you can see here all rows where number of violations is equal to zero are also zeros in binary violation however for all rows where number of violations is greater than zero binary violation is one an extension of this is perhaps you wish to group a numerical variable into more than two bins this is often useful for variables such as age wage brackets etc where exact numbers are less relevant than the general magnitude of the value considered the same data set of restaurant Health and Safety Ratings containing the number of times a restaurant has had major violations this time we will be creating three groups Group one for restaurants with no offenses group two from restaurants with one or two offenses and group three for all restaurants with three or more offenses Bin's are created using the pandas cut function you can define the intervals using the bins argument as shown here which in this case is a list of 4 values you can also pass a list of labels like so note as we want to include zero in the first bin we must set the leftmost edge to lower than that so all values between negative infinity and zero are labeled as 1 all values equal to 1 or 2 are labeled as 2 and all values greater than 2 are labeled as 3 now you know how to binarize and bin numeric columns it's time for you to put this into practiceas mentioned in the previous lesson most machine learning models require your data to be in numeric format however even if your raw data is all numeric there is still a lot you can do to improve your features numeric features can be used to represent a huge array of different characteristics and measurements pretty much anything that can be quantitatively measured can be recorded as numeric data for example age the price of an item counts and even spatial data such as coordinates depending on the use case numeric features can be treated in several different ways we will work through a few of the considerations and possible feature engineering steps to keep in mind when dealing with numeric data one of the first questions you should ask when working with numeric features is whether the magnitude of the feature is its most important trait or just its direction for example if you had a data set of restaurant health and safety railings containing the number of times a restaurant had major violations you might care far more about whether the restaurant had any major violations at all as you would rather not take any chances over whether it was a repeat offender looking at the Toei data set containing restaurant IDs and the number of times they had major violations we can see that some restaurants have no major violations but many have one or more we were be recreating a new binary column representing whether or not a restaurant committed any violation here we first create a new column binary violation and set it to zero then we use the dot block notation to find all rows where number of violations is greater than zero and set the binary violation column to one as you can see here all rows where number of violations is equal to zero are also zeros in binary violation however for all rows where number of violations is greater than zero binary violation is one an extension of this is perhaps you wish to group a numerical variable into more than two bins this is often useful for variables such as age wage brackets etc where exact numbers are less relevant than the general magnitude of the value considered the same data set of restaurant Health and Safety Ratings containing the number of times a restaurant has had major violations this time we will be creating three groups Group one for restaurants with no offenses group two from restaurants with one or two offenses and group three for all restaurants with three or more offenses Bin's are created using the pandas cut function you can define the intervals using the bins argument as shown here which in this case is a list of 4 values you can also pass a list of labels like so note as we want to include zero in the first bin we must set the leftmost edge to lower than that so all values between negative infinity and zero are labeled as 1 all values equal to 1 or 2 are labeled as 2 and all values greater than 2 are labeled as 3 now you know how to binarize and bin numeric columns it's time for you to put this into practice\n"