MLxtend library (Machine Learning extensions) has many interesting functions for everyday data analysis and machine learning tasks. Although there are many machine learning libraries available for Python such as scikit-learn, TensorFlow, Keras, PyTorch, etc, however, MLxtend offers additional functionalities and can be a valuable addition to your data science toolbox.
In this post, I will go over several tools of the library, in particular, I will cover:
A link to a free one-page summary of this post is available at the end of the article.
For a list of all functionalities this library offers, you can visit MLxtend’s documentation .
MLxtend library is developed by Sebastian Raschka (a professor of statistics at the University of Wisconsin-Madison). The library has nice API documentation as well as many examples.
You can install the MLxtend package through the Python Package Index (PyPi) by running
pip install mlxtend.
In this post, I’m using the wine data set obtained from the Kaggle. The data contains 13 attributes of alcohol for three types of wine. This is a multiclass classification dataset, and you can find the description of the dataset here.
First, let’s import the data and prepare the input variables X (feature set) and the output variable y (target).
For creating counterfactual records (in the context of machine learning), we need to modify the features of some records from the training set in order to change the model prediction . This may be helpful in explaining the behavior of a trained model. The algorithm used in the library to create counterfactual records is developed by Wachter et al .
You can create counterfactual records using create_counterfactual() from the library. Note that this implementation works with any scikit-learn estimator that supports the
predict() function. Below is an example of creating a counterfactual record for an ML model. The counterfactual record is highlighted in a red dot within the classifier’s decision regions (we will go over how to draw decision regions of classifiers later in the post).
The code to create a counterfactual record in a classifier’s decision regions (Source code: author)
A counterfactual record is highlighted within a classifier’s decision region (Image by author)
An interesting and different way to look at PCA results is through a correlation circle that can be plotted using plot_pca_correlation_graph(). We basically compute the correlation between the original dataset columns and the PCs (principal components). Then, these correlations are plotted as vectors on a unit-circle. The axes of the circle are the selected dimensions (_a.k.a. _PCs). You can specify the PCs you’re interested in by passing them as a tuple to
dimensions function argument. The correlation circle axes labels show the percentage of the explained variance for the corresponding PC .
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For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.
With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.
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Python is the most widespread and popular programming language in data science, software development, and related fields. The simplicity of codes in Python, which helps learners avoid any confusion, is the key to this popularity. Python has constantly been developing, and it keeps getting updated for more ease in using. With 137,000 plus libraries and tools, Python has always provided its users with the solutions to problems of any complexity level. This reason makes Python the ideal language for Data Science operations. This article focuses on some of the essential and must-learn libraries in Python used heavily by Data Scientists. I have tried to cover different libraries used in various stages of a data science cycle, such as Data Mining, processing and modeling, Data Visualization.
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Data mining is a world itself, which is why it can easily get very confusing. There is an incredible number of data mining tools available in the market. However, while some might be more suitable for handling data mining in Big Data, others stand out for their data visualization features.
As is explained in this article, data mining is about discovering patterns in data and predicting trends and behaviors. Simply put, it is the process of converting vasts sets of data into relevant information. There is not much use in having massive amounts of data if we do not actually know what it means.
This process encompasses other fields such as machine learning, database systems, and statistics. Additionally, data mining functions can vary greatly from data cleansing to artificial intelligence, data analytics, regression, clustering, etc. Consequently, many tools are being developed and updated to fulfill these functions and ensure the quality of large data sets (since poor data quality results in poor and irrelevant insights). This article seeks to explain the best options for each function and context. Keep reading to find out our 21 top mining tools!
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