What is Scikit Learn in Python

Want to learn Scikit learn in Python? Let us start with what is Scikit Learn in Python? how to install the scikit-learn library? how to update the scikit-learn library. And, we will cover these topics.

  • What is scikit learn in Python
  • History of scikit learn
  • The benefit of scikit learn
  • Advantage and disadvantages of scikit learn
  • Example of how to use Scikit Learn in Python
  • How to install scikit learn library
  • How to update scikit learn library
  • Features of scikit learn

What is scikit learn in Python

Scikit learn is a library that is used in machine learning and it focused on modeling the data. It only simply focus on modeling not focus on loading and manipulating the data.

Statical modeling includes classification, regression, and clustering via constancy interface in python.

Read Scikit-learn logistic regression

History of scikit learn

In this section, we will learn about the History of scikit learn, in which year the scikit learn come. who made this, we learn all things in brief.

  • Scikit learn is also known as sklearn. Scikit learn in python was first developed by David Cournapeau in the year 2007.
  • It was the part of Google Summer of Code project. Scikit learn was publicly released in 2010 with a v0.1 beta version which is very helpful for the programmers.
  • The latest version is 0.23.1 of the scikit learn is released in May 2020. After the release of the latest version, a driven project was made where anyone can contribute to his development.
  • The scikit learn is one of the most useful open-source and easy-to-use libraries that simplify the task of coding and help the programmer.
  • Scikit learn in python is mostly used in python for focusing on the modeling. It simply focused on modeling not focused on loading the data.

From this example, we can see that how scikit learn library work:

  • n_sample = 5000 is used to generate the sample of data.
  • centers_init, indices = kmeans_plusplus(X, n_clusters=4, random_state=0) is used to calculate the seed from k mean plus plus.
  • plot.figure(1) is used for plotting the seed on the screen.
  • plot.title(“K-Mean Clustering”) is used to give the title to the graph which is plotting on the screen.
from sklearn.cluster import kmeans_plusplus
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plot


n_sample = 5000
n_component = 4

X, y_true = make_blobs(
    n_samples=n_sample, centers=n_component, cluster_std=0.60, random_state=0
)
X = X[:, ::-1]


centers_init, indices = kmeans_plusplus(X, n_clusters=4, random_state=0)


plot.figure(1)
colors = ["red", "blue", "green", "yellow"]

for k, col in enumerate(colors):
    cluster_data = y_true == k
    plot.scatter(X[cluster_data, 0], X[cluster_data, 1], c=col, marker=".", s=10)

plot.scatter(centers_init[:, 0], centers_init[:, 1], c="b", s=50)
plot.title("K-Mean Clustering")
plot.xticks([])
plot.yticks([])
plot.show()

Output:

After running the above code we get the following output in which we can see that the seed is put in different clusters according to their need. The KMean_Plusplus is the default initialization of KMean.

scikit learn K Mean plus plus clustering
scikit learn K Mean plusplus clustering

Read: Scikit-learn Vs Tensorflow – Detailed Comparison

The benefit of scikit learn

In this section, we will learn about the benefits of scikit learn in Python.

The main benefits of scikit learn are its open-source anyone can use it any time this library and it is easy to use.

The benefit of scikit learn are :

  • Open source
  • Easy to use
  • Free
  • Properly documented
  • Versatile used

Here is a brief description of the benefits of scikit learn:

  1. Open source: scikit learn is an open-source library that is available for public use. This is openly available for use or even in this the user can modify or redirect the whole code.
  2. Easy to use: As we know scikit learn is an open-source library anyone can use this anytime. Many of the research organizations use scikit learn in their operation and they all were agreed that the scikit learn library is easy to use.
  3. Free: scikit learn library is free to use the people don’t take any license for running this library. The user would not have to worry when they work on the application.
  4. Properly Documented: In the scikit learn the documentation of the task is done in a proper way. Scikit learn library is extensive and has well-defined API documentation that is accessible from their website is provided.
  5. Versatile used: Scikit library is a user-friendly and handy tool it can do multiple things such as identifying the user actions, Predicting customer behavior which proves that it is versatile in nature.

Read Scikit learn accuracy_score

Advantage and disadvantages of scikit learn

Here we will illustrate the advantages and disadvantages of using scikit learn library in python.

Advantages:

  • The scikit learn library is a user-friendly and handy tool it can do multiple things such as prediction of customer behavior, creation of neuroimage, etc.
  • It is easy to use and free to use.
  • Scikit learn library is updated by the contributor and by the international online community.
  • Scikit learn library provides the API documentation for the user who wants to integrate algorithm with his platform.
  • The scikit learn library is spread under the BSD license which makes it free with legal and licensing restrictions the user can use it anytime without any hesitation and run this library on their platform.
  • In scikit learn the documentation in the task is done in the proper way and this library is very extensive.

Disadvantage:

Scikit learn is not the best choice for in-depth learning this is the disadvantage of this library.

Read: Scikit learn Decision Tree

Scikit learn in Python Example

In this example, we will work with the sklearn library. As we know sklearn is used to model the data. It only focuses on modeling the data it is not focused on manipulating the data.

  • from sklearn.datasets import load_iris is used to load the iris dataset and this iris dataset is already included in scikit-learn.
  • X = iris.data is used to store the feature matrix (X).
  • y = iris.target is used to response vector (y).
  • feature_names = iris.feature_names is used to store the feature name.
  • target_names = iris.target_names is used to store the target name.
  • print(“Feature names:”, feature_names) is used to print the feature of our dataset.
  • print(“Target names:”, target_names) is used to print the target of our dataset.
  • print(“\nType of X is:”, type(X)) in this X and y are the numpy array.
  • print(“\nFirst 5 rows of X:\n”, X[:5]) is used to print first five input rows.

from sklearn.datasets import load_iris
iris = load_iris()
  
X = iris.data
y = iris.target
  
feature_names = iris.feature_names
target_names = iris.target_names
  
print("Feature names:", feature_names)
print("Target names:", target_names)
  
print("\nType of X is:", type(X))
  
print("\nFirst 5 rows of X:\n", X[:5])

Output:

In the following output, we can see that with the help of sklearn library modeling of data is generated. Here we can see that the first five rows of X are printed on the screen.

Scikit learn example
Scikit learn example

Read: Scikit learn Feature Selection

How to install scikit learn

As we know scikit learn is used to focus on modeling data. For modeling our data we can install the scikit learn library.

Before installing the scikit learn we can install Numpy and scipy.

The following command can be used for installing the NumPy.

pip install numpy

In the following image, we can see that NumPy is already installed and it can satisfy all the requirements.

numpy installation
NumPy installation

The following command can be used for installing the scipy.

pip install scipy

In the following output, we can see that scipy is installed and also collecting all the packages of scipy.

scipy installation

If these two libraries are already installed then no need to install them again then move to a further step and install the scikit learn library.

pip install scikit-learn

In the following output, we can see that we install the scikit-learn library using pip and it satisfied all the requirements.

scikit learn installation

If all libraries are already installed when we don’t need to install them again we can simply use and run the command as per the need.

How to update scikit learn

As we know the scikit learn library is used to focus on modeling the data. We can simply install this library by just putting the pip install scikit-learn command. After installing the scikit-learn library we can also update it to give the latest version to the library.

The following command is used for updating the scikit-learn

pip install -U scikit-learn

In the following output, we can see that the scikit-learn library is updated and it satisfied all the requirements.

Updating scikit learn
Updating scikit learn

Features of scikit learn

As we know scikit learn library is used for focusing on modeling data not to focus on manipulating or summarizing data.

Here we will discuss the feature of the scikit learn library. The feature is a section attraction or we can say that important characteristic.

Here are the scikit learn library features which we will discuss further below:

  • Supervised learning
  • Unsupervised learning
  • Clustering
  • Dimension Reduction
  • Ensemble methods
  • Cross validation
  • Feature extraction
  • Feature selection
  • Open source
  1. Supervised learning: Supervised learning is predictive modeling it has a target variable and the data comes with the additional quality that we want to predict.

Supervised learning is further divided into two categories:

  • Classification
  • Regression

Classification: A problem is called the classification problem when we have the categorized output such as “black”, “white”, “teaching”, “Nonteaching”.Classification is also a predictive model it can categorize the given set of data into classes.

Regression: A problem is called the regression problem where the output is having the continuous output or we can say that it can only predict the continuous output. such as “distance”,”kilometer” etc.

2. Unsupervised learning: Unsupervised learning does not provide any superior to provide any sort of guidance. In unsupervised learning, data is without a label. Here we have an input variable x then there will be no corresponding output variable as there is in supervised learning. The model needs to allow it to work on its own discovery of information.

3. Clustering: A clustering problem is defined as where you want to discover the inherent or we can say that permanent grouping in data such as grouping customers by their purchasing behavior.

4. Dimension Reduction: Dimension in mathematics has measured the size or distance of an object. Dimension reduction is predictive modeling it can reduce the number of input variables from the dataset or we say can that dimensionality reduction reduces the input features.

5. Ensemble methods: The ensemble model is defined as it is also a machine learning technique that combines multiple models to form a predictive model or we can say that it is also a combination of prediction of multiple supervised models.

6. Cross-validation: Cross-validation is the feature of scikit learn it is used to look into the accuracy of the supervised model as we know supervised learning is the predictive modeling it has a target variable and data comes in the additional quality that we want to predict on unseen data.

7. Feature extraction: As the name suggested extract the feature from the data set and protect the information in the original data set or explain the attribute of text data.

8. Feature selection: It is used to select the feature from the data set and tunning down the predictor variable that is used by the model and is also used to recognize the attribute to make supervise model.

9. Open source: In this open-source is defined as any features or program code that is freely or openly available for use and even in this the user can modify or redirect the whole code. It is basically made for public use. Open-source software is computer software that is available for public use.

Conclusion

Scikit learn is helpful for beginners who want to solve the predicting problem such as supervised learning problem. The scikit learn can solve all the typical problems in a simple and easy way. All the academic, institutes, and industrial organizations use the scikit learn library for performing the various operation in a simple and easy way.

So, in this tutorial we discussed Scikit learn, and we have also covered different examples related to this library. Here is the list of examples that we have covered.

  • What is scikit learn in Python
  • History of scikit learn
  • The benefit of scikit learn
  • Advantage and disadvantages of scikit learn
  • Example of how to use Scikit Learn in Python
  • How to install scikit learn library
  • How to update scikit learn library
  • Features of scikit learn