Model Building in Scikit-learn: A Python Machine Learning Library

Model Building in Scikit-learn: A Python Machine Learning Library
Model Building in Scikit-learn: A Python Machine Learning Library

Scikit-learn is probably the most useful library for machine learning in Python. The sklearn library contains a lot of efficient tools for machine learning and statistical modelling including classification, regression, clustering, model selection, preprocessing and dimensionality reduction.

The predefined modules in sklearn are capable of transforming the raw data into different mathematical models. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation and API consistency.

Scikit is more for creating and building models, so one must have a basic understanding of various supervised and unsupervised models. Model evaluation metrics, underlying mathematical calculations. One should also be comfortable with the basics of python programming, and other commonly used libraries.

What importance can you get from sklearn ?

  • If you are a beginner you can work on datasets that are provided in sklearn here is the official link for the datasets and can explore the preprocessing models of sklearn.
  • If you have already worked on several datasets then you can move to Data Exploration, Data Visualisation One of the first and most important step in data analytics is to prepare and understand your data. Data exploration is done with an aim to find any anomaly in data, check if any transformation or feature re-engineering helps to predict/classify the target variables. We can check for any missing values in the dataset, any extreme or outliers etc.
  • If you have strong knowledge of Data Visualisation Techniques you can work on

Model Persistence: Various model building, K fold cross-validation, model output interpretation and saving/reusing the model are few of the other topics touched. Hopefully, it can provide some insights and clarity to anyone who is seeking to build up skills for data analytics.

Here are the main ways the Scikit-learn library is used.

Classification: The classification tools identify the category associated with the provided data. For example, classifying images and texts on social media platforms whether they are appropriate or not, they can be used to categorise email messages as either spam or not.

Classification algorithms in Scikit-learn include:

  • Support vector machines (SVMs)
  • Nearest neighbours
  • Random forest

The most famous model used in classification is SVM as it finds the best-suited path for the classification model. If You want to work on the model of SVM here is the official link for the support vector machine. The below graph shows the difference between a simple classifier and SVM. It finds the best possible classifying line for the data.

Regression: It involves creating a model that tries to comprehend the relationship between input and output data. Regression models have a tendency to bend their curves for a better approach for problem-solving. For example, regression tools can be used to understand the behaviour of stock prices.

Regression algorithms include:

  • SVMs
  • Ridge regression
  • Lasso

Lasso regression is a type of linear regression that uses shrinkage. Shrinkage is where data values are shrunk towards a central point, like the mean. The lasso procedure encourages simple, sparse models (i.e. models with fewer parameters). Lasso is considered as important model for the regression problems. The graph for the regression models is below and the official link is here.

Clustering: The Scikit-learn clustering tools are used to automatically group data with the same characteristics into sets. Classifying groups in data models are basically something that clustering deals with, for example, customer data can be segmented based on their localities.

Clustering algorithms include:

  • K-means
  • Spectral clustering
  • Mean-shift

Let’s explore K-Means

Clustering is a Machine Learning technique that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group.

The above graph shows three clusters of separate data with specific properties K-means is a scikit module that takes raw input of the mathematical form of data and binds them in clusters. It is basically a type of unsupervised learning method. An unsupervised learning method is a method in which we draw references from datasets consisting of input data without labelled responses. Generally, it is used as a process to find meaningful structure, explanatory underlying processes, generative features and groupings inherent in a set of examples.

Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them.

Dimensional Reduction: It lowers the number of random variables for analysis. For example, to increase the efficiency of visualisations, outlying data may not be considered.

Dimensionality reduction algorithms include:

  • Missing Values Ratio
  • Low Variance Filter
  • High Correlation Filter
  • Random Forests/Ensemble Trees
  • Principal Component Analysis (PCA)
  • Backward Feature Elimination
  • Forward Feature Construction

The number of input variables or features for a dataset is referred to as its dimensionality. Dimensionality reduction refers to techniques that reduce the number of input variables in a dataset. More input features often make a predictive modelling task more challenging to model, more generally referred to as the curse of dimensionality.

High-dimensionality statistics and dimensionality reduction techniques are often used for data visualisation. Nevertheless, these techniques can be used in applied machine learning to simplify a classification or regression dataset in order to better fit a predictive model.

Dimension reduction is generally performed to keep the important information only and curve the memory use for the dataset.

Model selection Tools: Model selection algorithms offer tools to compare, validate, and select the best parameters and models to use in your data science projects. Model selection modules that can deliver enhanced accuracy through parameter tuning include:

  • Grid search
  • Cross-validation
  • Metrics
  • K-fold

We can’t perform every machine learning algorithm on the same model as the data differs the need to change the model also comes into action so we take the help of the sklearn model selection tools, for example, I take Cross-validation module using this module we break the train data into segments and try to perform the classifier defined on the data and it gives the score for the fragments by this you can easily get an idea of how good your model is performing. Below is the flowchart explanation for K-fold.

Preprocessing: The Scikit-learn preprocessing tools are important in feature extraction and normalisation during data analysis. For example, you can use these tools to transform input data—such as text—and apply their features in your analysis.

Preprocessing modules include:

  • Preprocessing
  • Feature extraction
  • Scaling of data
  • Dealing with outliers
  • Data Exploration (EDA)

If there is much irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase is more difficult. Data preparation and filtering steps can take a considerable amount of processing time. Data preprocessing includes cleaning, Instance selection, normalization, transformation, feature extraction and selection, etc. The product of data preprocessing is the final training set.

If you want to go an extra mile and learn more about sklearn here is the link for documentation of sklearn.

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By Rohit Chauhan