In This Article, We Are Going To Use One Of The Most Powerful Tools That Can Use In Python To Implement Artificial Intelligence In The Shortest Time And Introduce You To The General Principles Of Design In This Field.

Scikit-Learn, Data scientists use artificial intelligence (AI) to perform a wide range of tasks. At present, control systems can reduce the energy consumption of buildings, make recommendations for buying clothes or watching movies and TV series, improve farming and irrigation practices, and even steer cars. Knowing how to use these tools will help you solve other important technical challenges in the community.

Fortunately, getting started with AI is not difficult for people who have already worked with Python and data analysis. You can use the powerful scikit-learn package to perform difficult programming processes.

What is scikit-learn?

scikit-learn is a Python package designed to facilitate the use of machine learning and artificial intelligence algorithms. The above package includes algorithms used for classification, regression and clustering, random forests, and slope upgrade algorithms. This software package is designed to interact well with other common scientific packages. Although not specifically designed to work with Pandas, it has a good relationship with Pandas.

In addition, scikit-learn includes useful tools to facilitate the use of machine learning algorithms. The design and development of data transfer passages used in machine learning, which significantly improve the accuracy of a system in predictions, requires the division of data into training and experimental sets and the scoring of algorithms to determine how well they perform and distinguish between appropriate and non-appropriate models. Suitable from each other. The scikit-learn interface includes tools for doing all of these tasks.

How does scikit-learn work?

The development and testing of skating learning algorithms can be divided into the following three general steps:

Educate the model using existing datasets and describe the phenomena you need to predict the model.

Test the model with other datasets to make sure it works well.

Use the model to predict phenomena.

The scikit-learn application programming interface provides instructions for performing each of these steps by calling a single function. All skate learning algorithms use the same function for this process, so you can use it in other projects if you are familiar with the basics. Learn it for one person. You learn it for everyone.

Calling a function to teach a skating learning algorithm is a .fit () function. To teach each model, you must call the above function and transfer two components of the training data set to it.

The two components are the data set x, the data that describes the properties of the data set, and the data y, which refers to information that describes the goals of the system (Features), and Targets are machine learning terms that basically mean Data are x and y).

The algorithm then creates a mathematical model selected by the algorithm. It then determines the model’s parameters to be as consistent as possible with the training data provided. The next step stores the parameters in the model and allows you to call the appropriate version of the model if needed for your project.

The model matching test function is called .score ().

To use this function, you must call it and send the data set x, which represents the properties, and the data set y, which represents the objectives. The important thing to note in this section is to use a different dataset called the experimental dataset to teach the model. When a model is evaluated based on practice data, it is likely to score very well because it must be mathematically consistent with that data set.

The real test is how the model performs well on different datasets because it is supposed to perform well on real datasets. When calling the function, the .score () function of the Skeleton packet returns the value of r² to show how the model predicted the data set y using the data set x.

Using the .predict () function of the above package, you can predict the output of a system according to the inputs provided. Another important point to note is that you should do the above process only after installing the model. Here is a fitting process, which shows how the model fits into the data set.

So if you do not do this, the model does not provide a valuable forecast. Once the task is done, you can then move an x ​​dataset to the .predict () function to return the model of a predicted y dataset as output. This way, you can predict how the system will behave in the future.

These three basic function functions form the Skeleton application programming interface and help you use artificial intelligence to solve technical problems.

How to create training and experimental data sets?

Creating a separate and experimental training data set is one of the most important parts of teaching artificial intelligence models. Without doing so, we can not create a model that fits the system we are trying to predict, and in addition, the accuracy of the predictions made cannot confirm. Fortunately, Skeetler has developed a useful tool to facilitate this process. This tool is called train_test_split ().

Train_test_split () does exactly what its name implies. The above function divides the received data set into educational and experimental data sets. Developers can use the above tools to build the data sets to ensure the designed model can predict correctly.

Here you need to provide a data set to train_test_split and prepare the required training and test datasets. It then divides the dataset into training and experimental datasets that you can use to develop your model.

There are a few things to keep in mind when using the above function.

First, train_test_split is random. The above function does not return the same training and experimental data set if it executes the same input data several times. If you want to test the model’s accuracy, the above approach is appropriate, but it may not be fascinating if you want to use the same data set in the model repeatedly. To ensure you get the same result each time you run, I suggest using the random_state parameter.

Setting the random mode forces it to use the same random kernel and provide the same data partitions every time you use it. Most developers set it to 42 when using random_state.

How do these tools work when combined?

Altogether, these tools provide a simple interface for building and using skating learning tools. It is better to use the example of the Skeleton regression linear model for understanding the subject better.

To implement this process, you need to call the tools needed to do this. These include the Skeleton model, the train_test_split () function, and Pandas for the data analysis process. The functions are used as follows:

from scikit-learn.linear_model import LinearRegression
from scikit-learn.model_selection import train_test_split
import pandas as PD

We can now use the dataset to train and test the model. You should now use the data set concerning the model you intend to create, similar to the following command.

data = PD.read_csv (‘Hamid_Reza_Taebi.csv’, index_col = 0)

Next, you need to divide the set data into X and y data and specify the properties you want to evaluate in columns. In this case, too, it is important to filter your data set to use data only at the moment. If we ignore this step, you will turn the nonlinear model into a linear model, failing.