The **code** that accompanies this article can be found **here.**

**Deep Learning **and **Machine Learning** are no longer a novelty. Many applications are utilizing the power of these technologies for cheap **predictions**, object detection and various other purposes. At this blog, we usually write about deep learning, but we felt the need to address some more standard Machine Learning techniques and algorithms and go back to where it all **started**. In this article, we start off simple with **Linear Regression**. It is a well-known algorithm and it is the basics of this vast field. Linear Regression is, sort of, the root of it all. We will address theory and math behind it and show how we can implement this simple algorithm using **several** different technologies.

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For the purpose of this article, make sure that you have installed the following *Python *libraries:

**NumPy**– Follow**this guide**if you need help with installation.**SciKit Learn**– Follow**this guide**if you need help with installation.**TensorFlow**– Follow**this guide**if you need help with installation.**Pytorch**– Follow**this guide**if you need help with installation.

Once installed make sure that you have imported all the necessary modules that are used in this tutorial.

Also, make sure that you are familiar with the basics of **linear algebra**, **calculus** and **probability**.

## Simple Linear Regression

Sometimes data that we have is quite **simple**. Sometimes, the output value of the dataset is just the **linear combination** of features in the input example. Let’s simplify it even further and say that we have only **one** feature in the input data. A mathematical model that describes such a **relationship** can be is presented with the formula:

For example, let’s say that this is our data:

In this particular case, the mathematical model that we want to create is just a **linear function** of the input feature, where *b0* and *b1* are the model’s **parameters**. These parameters should be learned during the training process. After that, the model should be able to give correct output **predictions** for new inputs. To sum it up, during training we need to learn *b0* and *b1 *based on the values of *x* and *y*, so our *f(xi)* is able to return correct predictions for the **new** inputs. If we want to **generalize** even further we can say that model makes a prediction by adding a constant (bias term – *b0*) on the precomputed weighted sum (*b1)* of the input features. However, let’s back to our example and clear things up a little bit before we dive into generalization. Here is what the aforementioned data looks like on the **plot**:

Our linear regression model, by calculating optimal *b0* and *b1,* produces a line that will best fit this data. This line should be optimally **distanced** from all points in the graph. It is called the **regression line**. So, how does the algorithm calculates *b0* and *b1* values?

In the formula above, *f(xi)* represents the predicted output value for ith example from the input, and *b0* and *b1* are regression coefficients that represent the **y-intercept** and **slope** of the regression line. We want that value to be as close as possible to the real value – *y*. Thus model needs to learn the values regression coefficients *b0* and *b1*, based on which model will be able to predict the correct output. In order to make these estimates, the algorithm needs to know how bad are his **current** estimations of these coefficients. At the beginning of the training process, we feed samples into the algorithm which calculates output *f(xi)* of the current sample, based on **initial** values of regression coefficients. Then the error is **calculated** and coefficients are corrected. Error for each sample can be calculated like this:

Meaning, we **subtract** estimated output from the real output. Note that this is a training process and we **know** the value of the output in the i-th sample. Because *ei* depends on coefficient values it can be described by the **function**. If we want to minimize *ei* and for that, we need to define a function based on which we will do so. In this article, we use the **Least Squares Technique** and define the function that we want to minimize as:

The function that we want to minimize is called the **objective function** or **loss function**. In order to minimize *ei*, we need to find coefficients *b0* and *b1* for which *J* will hit the global minimum. Without going into mathematical details (you can check out that **here**), here is how we can calculate values for *b0* and *b1:*

Here *SSxy *is the sum of cross-deviations of y and x:

while *SSxx* is the sum of squared deviations of x:

Ok, so much for the theory, let’s implement this algorithm using *Python*.

## Simple Linear Regression Python Implementation

The **code** that accompanies this article can be found **here.**

The algorithm we discussed previously is implemented withing *SimpleLinearRegression* class:

This class has two functions, of which *fit *method is extremely **interesting**. In this method, we first **calculate** the mean values of input data and expected output. Then we calculate *SSxy* and *SSxx* and utilize those values to calculate *b0 * and *b1. *Basically, based on the input data we calculated necessary values for *b0* and *b1*, ie. this is how we created our **model**. We can consider the *fit *method as our **training** function. The *predict* method uses these values to calculate values for the new data. Here is how we can use this function on the data from the previous chapter:

Then we use the **trained model** to predict new values and plot regression line:

## Multiple Linear Regression

Ok, that was super simple. The usage of this example is very limited since we usually end up with **datasets** with more **features** in them. Let’s take it up a notch and get a little more practical…and mathematical. We observe a set of labeled samples* {(xi, yi)} Ni=1*. The *N* is the size of the set, while *xi* is the *D*-dimensional feature vector and *yi* is the output. Every feature x is the real number. One such dataset is the famous **Boston Housing Dataset**. Once we load it with *Pandas*, we get something like this:

In this dataset, the **output** is the *medv* feature, while the rest of the features are **input** features. As you can see there are several features *(x1 – crim, x2-zn,….)* for each sample* i*. Now, we can **generalize** principles of linear regression and use them on a dataset with more features. We can present the model with a formula:

Or to simplify it even further:

We changed the notion there a little bit, but it is essentially the **same** as the previous formula, it is just **vectorized**. The bias *b0* became *b*. The *w* is now a *D*-dimensional vector (because we have a *D* number of features, remember) of parameters. To predict the *y* for a given *x* we use this model. Obviously, we want to find the optimal values for coefficients (*w, b*) for which the model will output accurate predictions. Unlike simple linear regression which creates a line, multiple linear regression creates a **hyperplane**, since every feature represents one **dimension**. This hyperplane is chosen like that to be as **close** to all sample values as possible. To calculate the optimal coefficient, this time we want to minimize **Mean Squared Error** function:

To quickly find the values of w and b that minimize MSE we use the so-called **Normal Equation**. This equation gives direct results for mentioned coeficients:

Ok, let’s utilize this in the code.

## Multiple Linear Regression Python Implementation

The **code** that accompanies this article can be found **here.**

The algorithm we discussed previously is implemented withing *MultipleLinearRegression* class:

The ‘*blueprint*‘ is the same as for the previous implementation, we have two functions *fit* and *predict*. The first one **creates** the model, while the other **utilizes** it. In the *fit* method, we first reshape input data if it has only one dimension. This way we **incorporate** the implementation of Simple Linear Regression in this class as well. Then we extend input data with a vector of ones. This is done because the bias coefficient – *b* is not implicitly modeled in data, i.e. this value is not multiplied with any feature from the input data so we need to add it **manually**. So, we add an array of ones so this value is calculated as well. After that, we utilize the **Normal Equation** and calculate all the coefficients. Here is how we can use this class on the Boston Housing Dataset:

First, we **separate** input data – X from the output data – y. Then we create an **object** of *MultipleLinearRegression* class and call *fit* method with input data. Finally, we call the *predict* method to generate **predictions**. To compare predictions made by our implementation with the actual values, we can create *Pandas Dataframe*:

From the results, we can see that overall results are **close** but not too close to the real results. Keep in mind that we haven’t performed any data **preprocessing** and that we haven’t scaled the data. Let’s see how we can do that and use *SciKit Learn* implementation of the same algorithm.

## Using SciKit Learn

Our previous implementations are quite vanilla. If we try to use them on a **large** dataset we may encounter some problems. For starters, it loads all the data, so we can only hope that we have enough **memory** for the whole dataset. Apart from that, this is really computing **expensive** ways to perform Linear Regression. The computational complexity of inverting a matrix of n features (which normal equation does) is typically about O(n2.4) to O(n3). This means that if we have a larger dataset, which has twice as many features (2*n) computation time will be 5.3 to 8 times **larger**.

We haven’t supported **gradient descent** or some other smart optimization technique. Lucky for us, guys from *SciKit Learn* did. *SciKit Learn* is a great **tool** for machine learning in *Python.* Using it we can not only use algorithms out of the box but **preprocess** data as well. So let’s first fix several things we overlooked in the previous implementation. We need to split our data into training and testing sets, this is mandatory. Apart from this, we can compare the results of our implementation with the out-of-the-box solution that *SciKit Learn* provides.

Here is the output:

We can see that we have the **same** results using both implementations. Also, we can see that results are not that bad. Can we improve them? We can try using *SGDRegressor*. This is a Linear Regression Model that uses Stochastic Gradient Descent for the **optimization**. If we want to use this algorithm we must scale data. For that purpose we utilize *SciKit Learn’s Pipeline, StandardScaler and SGDRegressior*:

First, we create **pipeline** of *StandardScalerI and **SGDRegressor.* This means that when we use *fit* method of this pipeline, data will first be **processed** by the scaler and then regressor with be trained. Here is the comparison of the results:

We can see that results are **mixed**. Sometimes we get better results using the standard approach, while others we get better results using Stochastic Gradient Descent.

## Using Deep Learning Frameworks

Alternatively, we may want to pick some **deep learning** frameworks for the implementation of Linear Regression with Stochastic Gradient Descent. In this article, we use *TensorFlow* and *PyTorch*. Note that we are **not** using neural networks, but we use these frameworks to implement Linear Regression from **scratch**. Thus it is making this part of the article more of a thought exercise than something you would regularly do. If you want to get more familiar with *TensorFlow* take a look at **this article**, while if you want to learn more about PyTorch check out this **mini-series of articles**.

### Using TensorFlow

So far we got a pretty clear idea of how the classes should look like. Here is implementation of Linear Regression with Stochastic Gradient Descent with *TensorFlow* – *TensorFlowLinearRegression*:

This is really similar to our **initial** implementation of Linear Regression, but it uses gradient descent. The core of the functionality is, of course, in the *fit* method. Notice that for this algorithm we need to define the number of **epochs**. When we push our complete dataset through the algorithm we have performed one epoch. In our implementation, we use *GradientTape *where we **record** operations for automatic differentiation. We calculate loss, for which we utilize the implementation of MSE with *TensorFlow*. Then we calculate **gradients** for *w* and *b* based on that loss. Finally, we align *w* and *b*. Here is how this class is used with *Boston Household Dataset* and the results we get once we use it:

As in the previous example, results are mixed, but it seems that vanilla implementation is somewhat better.

### Using PyTorch

Ok, let’s do the completely same thing with *PyTorch*. This implementation is done in *PyTorchLinearRegression:*

As you can notice, everything is completely the **same** except that we used *PyTorch. *The main difference we can see when we run this code. You will notice that *PyTorch* performs this process **faster**. Usage is the same as for the previous classes:

## Conclusion

In this article, we had a chance to see what is Linear Regression and how it is performed. We could see how we can implement our own algorithm or use the one that is available in SciKit Learn. Apart from that, we could see how one can implement this algorithm with Stochastic Gradient Descent and deep learning frameworks.

Thank you for reading!

#### Nikola M. Zivkovic

CAIO at Rubik's Code

Nikola M. Zivkovic a CAIO at **Rubik’s Code** and the author of book “**Deep Learning for Programmers**“. He is loves knowledge sharing, and he is experienced speaker. You can find him speaking at meetups, conferences and as a guest lecturer at the University of Novi Sad.

**Rubik’s Code** is a boutique data science and software service company with more than 10 years of experience in Machine Learning, Artificial Intelligence & Software development. Check out the **services **we provide.

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