The Importance of Feature Engineering in Machine Learning

The choice of features can have a huge impact on your learning average performance. In fact, for many practical applications, choosing or entering the right features is a critical step to making the algorithm work well. Feature engineering is an essential aspect of machine learning that involves using knowledge or intuition about the problem to design new features. This process allows you to transform or combine the original features of the problem in order to make it easier for the learning algorithm to make accurate predictions.

Predicting House Prices as a Case Study

Let's revisit the example of predicting the price of a house. You have two features for each house: X1, which is the width of the lot size of the plot of land that the house is built on, and X2, which is the depth of the lot size of the rectangular plot of land that the house was built on. Based on this information, you might build a model like this: f(X) = W1X1 + W2X2 + b, where X1 is the frontage or width, X2 is the depth, and b is the bias term. This model might work okay, but let's explore an alternative way to use these features that could be even more effective.

A New Perspective: The Area of the Land

You might notice that the area of the land can be calculated as the frontage or width times the depth. You may have an intuition that the area of the land is more predictive of the price than the frontage and depth as separate features. Based on this insight, you can define a new feature X3 as X1 times X2, which equals the area of the plot to land. With this new feature, you can then build a model like this: f(X) = W1X1 + W2X2 + w3X3 + b, where X1 is the frontage or width, X2 is the depth, X3 is the area, and b is the bias term.

The Power of Feature Engineering

By creating new features like X3, you are applying feature engineering to your problem. This technique allows you to transform or combine original features in order to make it easier for the learning algorithm to make accurate predictions. Depending on your insights into the application, defining new features can lead to a much better model. In fact, one flavor of feature engineering enables you to fit not just straight lines but curves non-linear functions to your data.

The Next Step: Non-Linear Functions

In the next video, we'll explore how you can use feature engineering to fit non-linear functions to your data. This will take us beyond simple linear models and allow us to tap into the full potential of machine learning. By mastering the art of feature engineering, you'll be able to unlock more accurate predictions and gain a deeper understanding of your data. So, join us next time as we delve into the world of non-linear functions and explore how you can use feature engineering to make your models truly powerful.

"WEBVTTKind: captionsLanguage: enthe choice of features can have a huge impact on your learning average performance in fact for many practical applications choosing or entering the right features is a critical step to making the algorithm work well in this video let's take a look at how you can choose or engineer the most appropriate features for your learning algorithm let's take a look at feature engineering by revisiting the example of predicting the price of a house say you have two features for each house X1 is the width of the lot size of the plot of land that the house is built on this in real estate is also called the frontage of the lot and the second feature X2 is the depth of the lot size of let's assume the rectangular plot of land that house was built on given these two features X1 and X2 you might build a model like this where f of x is W1 X1 plus W2 X2 plus b where X1 is the frontage or width and X2 is the depth and this model might work okay but here's another option for how you might choose a different way to use these features in the model that could be even more effective you might notice that the area of the land can be calculated as the frontage or width times the depth and you may have an intuition that the area of the land is more predictive of the price then the frontage and depth as separate features so you might Define a new feature X3 as X1 times X2 so this new feature X3 is equal to the area of the plot to land with this feature you can then have a model FWB of x equals W1 X1 plus W2 X2 plus w3x3 plus b so that the model can now choose parameters W1 W2 and W3 depending on whether the data shows that the frontage or the depth or the area X3 of the lot turns out to be the most important thing for predicting the price of the house what we just did creating a new feature is an example of what's called feature Engineering in which you might use your knowledge or intuition about the problem to design new features usually by transforming or combining the original features of the problem in order to make it easier for the learning algorithm to make accurate predictions so depending on what insights you may have into the application rather than just taking the features that you happen to have started off with sometimes by defining new features you might be able to get a much better model so that's future engineering and it turns out that there's one flavor of future engineering that allows you to fit not just straight lines but curves non-linear functions to your data let's take a look in the next video at how you can do thatthe choice of features can have a huge impact on your learning average performance in fact for many practical applications choosing or entering the right features is a critical step to making the algorithm work well in this video let's take a look at how you can choose or engineer the most appropriate features for your learning algorithm let's take a look at feature engineering by revisiting the example of predicting the price of a house say you have two features for each house X1 is the width of the lot size of the plot of land that the house is built on this in real estate is also called the frontage of the lot and the second feature X2 is the depth of the lot size of let's assume the rectangular plot of land that house was built on given these two features X1 and X2 you might build a model like this where f of x is W1 X1 plus W2 X2 plus b where X1 is the frontage or width and X2 is the depth and this model might work okay but here's another option for how you might choose a different way to use these features in the model that could be even more effective you might notice that the area of the land can be calculated as the frontage or width times the depth and you may have an intuition that the area of the land is more predictive of the price then the frontage and depth as separate features so you might Define a new feature X3 as X1 times X2 so this new feature X3 is equal to the area of the plot to land with this feature you can then have a model FWB of x equals W1 X1 plus W2 X2 plus w3x3 plus b so that the model can now choose parameters W1 W2 and W3 depending on whether the data shows that the frontage or the depth or the area X3 of the lot turns out to be the most important thing for predicting the price of the house what we just did creating a new feature is an example of what's called feature Engineering in which you might use your knowledge or intuition about the problem to design new features usually by transforming or combining the original features of the problem in order to make it easier for the learning algorithm to make accurate predictions so depending on what insights you may have into the application rather than just taking the features that you happen to have started off with sometimes by defining new features you might be able to get a much better model so that's future engineering and it turns out that there's one flavor of future engineering that allows you to fit not just straight lines but curves non-linear functions to your data let's take a look in the next video at how you can do that\n"