Hello everyone! Today I want to write about the Sci-kit-learn Library, it is commonly known as (Sklearn).
It is a useful and robust library for machine learning in Python. It provides a selection of tools for machine learning and statistical modeling by featuring various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, and k-means.
Sci-kit learn is build on top of the following libraries.
- SciPy is an ecosystem that consists of various libraries for completing technical computing tasks.
- Matplotlib is a library used for plotting various charts and graphs.
- NumPy It is a library that manipulates multi-dimensional arrays and matrices and has an extensive compilation of mathematical functions for performing various calculations.
Why sci-kit learn?
Sci-kit learn features various classification, regression, and clustering algorithms including support vector machines, random forests, gradient boosting, k-means, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
Ways in which Sci-kit learn is used.
- Selection of a Model
This is the act of classifying. It is the systematic arrangement in groups or categories according to an established criteria. When it comes to data science classification is used to identify the categories that are associated with data. This process requires tools that work together in the grouping of data.
The need for classification enables you to see how well your data fits into the dataset’s predefined categories so that you can then build a predictive model for use in classifying future data point. With sci-kit learn classification algorithms, you use dataset and use what you know about it to generate a predictive model.
The Classification algorithms in Scikit-learn are:
- Support vector machines (SVMs)
SVMs are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.
- Nearest neighbors
In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method proposed by Thomas Cover used for classification and regression.
- Random forest
Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes or mean prediction of the individual trees
Regression is an important and mostly used statistical and machine learning tool. Regression analysis is a set of statistical processes used for estimating the relationships between a dependent variable and one or more independent variables. A common form of regression analysis is linear regression, where a researcher finds the line that most closely fits the data according to a specific mathematical criteria. Regression-based tasks are used to predict output labels or responses that are continuous numeric values, for the given input data.
Regression involves the use of models that try to understand the relationship between an input and an output of a dataset.
Types of Regression
Simple regression model − it is the most basic regression model in which predictions are formed from a single, feature of the data.
Multiple regression model − this is a regression model where predictions are formed from multiple features of the data.
Clustering is a method used to find the similarity as well as the relationship, and patterns among data samples and then cluster those samples into groups having similarities based on the features and patterns. This enables you to discover the patterns in a data set for instance, in a hotel we can group a data based on the months where the bookings are high or low. This helps you to see that in the month of December the number of bookings is high compared to the month of march or January.
The photo below shows clustering system by grouping the images based on the different shapes available.
Data preprocessing this is a data mining technique that involves the transformation of raw data into an understandable format. SK learn package provides several utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for the downstream estimators. Preprocessing tools are used in feature extraction and normalization during data analysis.
Modules used in preprocessing are; Feature selection and preprocessing
Preprocessing is a data mining technique that involves the transformation of raw data into an understandable format, while feature selection is the process of selecting a subset of relevant features to be in the model construction.
5. Model Selection
This is the task of selecting a statistical model for deployment on a dataset. This involves the selection of parameters that results to a fit. Model selection algorithms tools enable the comparison, validation, and selection of the best parameters and models to use in your data.
Given that models have some error due to the noise in the data, incomplete data and other kinds of limitations getting the perfect model is no longer an option. This leaves us to get a good enough model for deployment.
Sklearn offers the following modules in model selection.
Also known as rotation estimation or out-of-sample testing. It is a model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set
- Grid search
It builds a model for every combination of hyperparameters specified and evaluates each model.
As we have seen Scikit-learn is a machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities. Therefore whenever you set up a task in machine learning be sure to use the Sk-learn library as it offers the tools you will need.
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