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The 10 Best Machine Learning Algorithms for Data Science Beginners

(This post was originally published on KDNuggets as The 10 Algorithms Machine Learning Engineers Need to Know. It has been reposted with perlesson, and was last updated in 2019).

This post is targeted towards beginners. If you’ve got some experience in data science and machine learning, you may be more interested in this more in-depth tutorial on doing machine learning in Python with scikit-learn, or in our machine learning courses, which start here. If you’re not clear yet on the differences between “data science” and “machine learning,” this article offers a good explanation: machine learning and data science — what makes them different?

Machine learning algorithms are programs that can learn from data and improve from experience, without human intervention. Learning tasks may include learning the function that maps the input to the output, learning the hidden structure in unlabeled data; or ‘instance-based learning’, where a class label is produced for a new instance by comparing the new instance (row) to instances from the training data, which were stored in memory. ‘Instance-based learning’ does not create an abstraction from specific instances.

There are 3 types of machine learning (ML) algorithms:

Supervised learning uses labeled training data to learn the mapping function that turns input variables (X) into the output variable (Y). In other words, it solves for f in the following equation:

Y = f (X)

This allows us to accurately generate outputs when given new inputs.

We’ll talk about two types of supervised learning: classification and regression.

Classification is used to predict the outcome of a given sample when the output variable is in the form of categories. A classification model might look at the input data and try to predict labels like “sick” or “healthy.”

Regression is used to predict the outcome of a given sample when the output variable is in the form of real values. For example, a regression model might process input data to predict the amount of rainfall, the height of a person, etc.

The first 5 algorithms that we cover in this blog — Linear Regression, Logistic Regression, CART, Naïve-Bayes, and K-Nearest Neighbors (KNN) — are examples of supervised learning.

Ensembling is another type of supervised learning. It means combining the predictions of multiple machine learning models that are individually weak to produce a more accurate prediction on a new sample. Algorithms 9 and 10 of this article — Bagging with Random Forests, Boosting with XGBoost — are examples of ensemble techniques.

Unsupervised learning models are used when we only have the input variables (X) and no corresponding output variables. They use unlabeled training data to model the underlying structure of the data.

We’ll talk about three types of unsupervised learning:

Association is used to discover the probability of the co-occurrence of items in a collection. It is extensively used in market-basket analysis. For example, an association model might be used to discover that if a customer purchases bread, s/he is 80% likely to also purchase eggs.

Clustering is used to group samples such that objects within the same cluster are more similar to each other than to the objects from another cluster.

Dimensionality Reduction is used to reduce the number of variables of a data set while ensuring that important information is still conveyed. Dimensionality Reduction can be done using Feature Extraction methods and Feature Selection methods. Feature Selection selects a subset of the original variables. Feature Extraction performs data transformation from a high-dimensional space to a low-dimensional space. Example: PCA algorithm is a Feature Extraction approach.

Algorithms 6–8 that we cover here — Apriori, K-means, PCA — are examples of unsupervised learning.

Reinforcement learning is a type of machine learning algorithm that allows an agent to decide the best next action based on its current state by learning behaviors that will maximize a reward.

Reinforcement algorithms usually learn optimal actions through trial and error. Imagine, for example, a video game in which the player needs to move to certain places at certain times to earn points. A reinforcement algorithm playing that game would start by moving randomly but, over time through trial and error, it would learn where and when it needed to move the in-game character to maximize its point total.

This is used to estimate real values like the cost of houses, number of calls, total sales, and many more based on a continuous variable. In this process, a relationship is established between independent and dependent variables by fitting the best line. This best fit line is called the regression line and is represented by a linear equation Y= a *X + b.

In this equation:

The coefficients a & b are derived depending on minimizing the sum of squared difference of distance between data points and regression line.

This is used to evaluate discrete values (mainly Binary values like 0/1, yes/no, true/false) depending on the available set of the independent variable(s). In simple terms, it is useful for predicting the probability of happening of the event by fitting data to a logit function. It is also called logit regression.

These below listed can be tried in order to improve the logistic regression model

This is highly used for classification problems. The Decision Tree algorithm is considered among the most popular machine learning algorithm. This perfectly works for both continuous and categorical dependent variables. This is done depending on the most significant attributes/ independent variables to create as distinct groups as possible.

Feature selection: Select a feature from the features of the training data as the split standard of the current node. (Different standards generate different decision tree algorithms.)

Decision tree generation: Generate internal node upside down based on the selected features and stop until the dataset can no longer be split.

Pruning: The decision tree may easily become overfitting unless necessary pruning (including pre-pruning and post-pruning) is performed to reduce the tree size and optimize its node structure.

There can be many hyperplanes that can do this task but the objective is to find that hyperplane that has the highest margin that means maximum distances between the two classes, so that in future if a new data point comes that is two be classified then it can be classified easily.

Over the last few years, huge amounts of data are been captured at every possible stage and are getting analyzed by many sectors. The raw data also consists of many features but the major challenge is in identifying highly significant variable(s) and patterns. The dimensionality reduction algorithms like Decision Tree, PCA, and Factor Analysis help find the relevant details depending on the correlation matrix, missing value ratio.

Dimensionality reduction technique can be defined as, “It is a way of converting the higher dimensions dataset into lesser dimensions dataset ensuring that it provides similar information.” These techniques are widely used in machine learning for obtaining a better fit predictive model while solving the classification and regression problems.

6. Gradient Boosting algorithms and AdaBoosting Algorithm

GBM — These are boosting algorithms that are highly used when huge loads of data have to be taken care of to make predictions with high accuracy. AdaBoost is an ensemble learning algorithm that mixes the predictive power of various base estimators to improve robustness.

XGBoost  This has a major high predictive analysis that makes it the most suitable choice for accuracy in events as it possesses both tree learning algorithms and linear models.

LightGBM  This is a gradient boosting framework that uses tree-based learning algorithms. The framework is a very quick and highly efficient gradient boosting one based on decision tree algorithms. It is designed to be distributed with the mentioned benefits:

CatBoost  This is an open-sourced machine learning algorithm. It can easily integrate with deep learning frameworks such as Core Ml and TensorFlow. It can work on various data formats.

Whoever is seeking a career in machine learning should understand and increase their knowledge of these algorithms.

How much is Student Insurance?

The 10 Best Machine Learning Algorithms for Data Science Beginners

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