When data features are deployed to train a machine, greater performance can be achieved through it. The reason behind this approach is to make humans understand and respond to the data more efficiently. The application of Artificial Intelligence is targetedtowards making algorithms more responsive towards the given data, in the sameway, humans are. An intriguing fact in this direction is that when theArtificial Intelligence succeeds beyond a point, Features Visualization willbecome redundant. The development of Artificial Intelligence works inconjunction with the relationship between algorithms and data.
The non-relevant features or the features that are partially relevant depict a negative impact on the performance of the model. The process of selecting some features from the data, so that they contribute more towards the output, is known as Feature Selection. The process is followed to reach a more favoured or intended output from the data. If there are too many irrelevant features in the data, it can cause inaccuracy in results. This is more prone to linear algorithms such as linear and logistic regression.
Various Tools Deployed in Feature Visualization
The best way to explain this part is with the common example of a restaurant. When we go to a restaurant, before ordering the food, we go through the menu and understand more about the options. Similarly, there are various tools, which are adapted to gain more understanding about the data and then make decisions.
The use of statistical tests can be implemented to select pre-determined features having the strongest relationship with the output variable. The ScikitLearn feature allows narrowing down on the best class that can be implemented in the suite having different statistical tests in order to select a specific number of features.
The type of data visualization, which is based on matplotlib is known as Seaborn. The features help in segregating the given data through informative and attractive statistical graphics. It is a dataset-oriented API used for examination of the relationship among multiple variables. The categorical variables are shown through specialized support of observations or aggregate statistics. The data is visualized as univariate or bivariate and is compared between subsets of data. The plotting function in Seaborn is dataset-oriented and operates on data-frames and arrays that contain whole datasets. The necessary semantic mapping is internally performed by statistical aggregation by producing informative plots.
Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits. Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc., with just a few lines of code.
Feature visualization is a very detailed topic, and it needs deeper understanding before application. Every tool of feature visualization plays an important role and provides a better understanding of the nature of the data. Feature visualization makes machine learning more feasible.