Top Machine Learning (ML) Algorithms to Learn in 2022

Artificial intelligence is rapidly becoming the present and the future of technology. Machine learning algorithms were created to handle difficult real-world situations. These algorithms are very efficient and self-modifying, as they improve over time with the addition of more data and minimal human involvement. Let’s review the top machine learning algorithms you should know about to keep up with the latest advances in ML.

1. Linear regression: This supervised learning algorithm estimates values ​​such as house prices and total sales using continuous variables. The algorithm describes the relationship between two variables, one independent and the other dependent. When the independent variable is changed, it affects the dependent variable. We can relate the independent and dependent variables by fitting the optimal line.

The regression line is the line of best fit and is represented by the linear equation:


Here, Y is the output variable, X is the input variable, and a and b are the intercept and slope of the line, respectively.

The purpose of linear regression is to determine the values ​​of the coefficients a and b and to find the best line of fit for the data.

2. Logistic regression: Unlike linear regression, logistic regression is used to predict discrete values. It is well suited to binary classification, in which an event is classified as 1 if it occurs and 0 if it does not occur.

The result of the logistic regression is a set of probabilities as the output, which is between 0 and 1. So, if we are trying to predict whether or not a candidate wins an election, where a win is given by 1, and a loss as 0, and our algorithm provides a candidate with a score of 0.95, it estimates that this candidate has a high chance of winning.

The value y is obtained by using the logistic function h(x)= 1/ (1 + ex) to log the value x. The probability is then forced into a binary categorization by applying a threshold.

3. Decision tree algorithm: It is a supervised learning algorithm commonly used to solve classification problems. This works for both categorical and continuous data. Using a tree-based methodology, all alternative outcomes of a decision are displayed. Inner nodes represent tests on various qualities, branches represent test results, and leaf nodes represent the choice reached after all attributes have been computed.

4. Random forest algorithm: Random Forest is a collection of decision trees. The random forest algorithm overcomes some of the drawbacks of the decision tree algorithm, namely that as the number of decisions in the tree increases, the accuracy of the result decreases. We have a collection of decision trees in Random Forest. Each tree categorizes a new object based on attributes, and we say the tree “votes” for that class, and the classification with the most votes is selected.

5. BASKET: CART (Classification and Regression Trees) implements decision trees. The root and internal nodes are nonterminal nodes in the classification and regression trees. Leaf nodes are terminal nodes. Leaf nodes represent the output variable, while non-terminal nodes represent a single input variable and a split point on that variable. To create predictions, the model is used as follows: to access a leaf node, iterate through the tree divisions and display the value existing at the leaf node.

6. Supports vector machine algorithm: Support Vector Machine Algorithm can be used for classification or regression problems. By locating a particular line (hyperplane) that divides the data set into different classes, the data is separated into different classes. The support vector machine algorithm searches for the hyperplane that maximizes the distance between classes (maximizing margin), thereby increasing the probability of correctly categorizing the data. The SVM is two-dimensional at low feature levels, but becomes three-dimensional as the number of detected groups or types increases.

7. Naive Bayes Classifier Algorithm: It is a classification method based on Bayes’ theorem and the assumption of predictor independence. A Naive Bayes classifier, in simple terms, posits that the existence of a feature in a class is independent of any other component. That’s why he is considered naive. Gmail uses this algorithm to determine whether an email is spam or not.

8. K Nearest Neighbors (KNN) Algorithm: It can solve classification and regression problems. It is, however, more commonly used in categorization problems. The algorithm separates data points into different classes using a comparable measure like the distance function. The output variable is then summarized for these K occurrences. A prediction for a new data point is created by searching the full data set for the K most comparable (neighboring) examples. It can be the average of the results in a regression problem or the mode in a classification problem. Note that this algorithm is computationally expensive and requires normalization of variables.

9. K stands for clustering algorithm: It is a type of unsupervised iterative machine learning algorithm that divides data into clusters based on similarity. It creates k cluster centroids and assigns a data point to the cluster whose centroid is closest to the data point. K-Means Clustering is a primary tool in consumer analysis.

10. Principal component analysis (PCA): By reducing the number of variables, principal component analysis (PCA) facilitates data analysis and visualization. This is accomplished by recording the highest variation of the data in a new coordinate system with axes called principal components.

11. Dimensionality reduction algorithms: With so much data available, we are faced with a plethora of options, which looks great for building a robust model, but presents obstacles such as identifying the most critical variables. In such circumstances, the dimensionality reduction approach, combined with other algorithms such as decision tree, random forest, PCA, missing values ​​ratio, factor analysis and others, can to be beneficial.

12. Gradient boosting algorithms:

  • WBG: Boosting is a set of learning techniques that combine predictions from several different baseline estimators to increase resilience on a single estimator. It builds a strong predictor by combining many weak or poor predictors.GBM is a reinforcement algorithm used when there is a lot of data, and we need to build a prediction with high accuracy.
  • XGBoost: The XGBoost has enormous predictive ability, making it the ideal choice for various competitions. It supports multiple objective functions including regression, classification, and ranking. It incorporates a linear model and a tree learning method, which makes the approach ten times faster than existing gradient boosting techniques. With cross-validation built into every iteration of the boosting process, XGBoost can be paired with Spark, Flink, and other cloud dataflow systems.
  • LightGBM: LightGBM uses tree-based learning algorithms to increase the gradient. The framework is based on decision tree algorithms which can be used for ranking, classification and various other machine learning applications. It is ready to handle massive amounts of data while improving accuracy. Faster training speed and efficiency, lower memory consumption, parallel and GPU learning capabilities are some of the advantages offered by LightGBM.
  • CatBoost: Yandex’s CatBoost is a robust open source machine learning algorithm. It is simple to integrate with deep learning frameworks like Google’s TensorFlow and Apple’s Core ML. The best part about CatBoost is that unlike other ML models, it doesn’t require substantial data training and can work with many different types of data.

13. Generative Adversarial Networks (GANs): GANs, or Generative Adversarial Networks, are a type of generative modeling that uses deep learning techniques such as convolutional neural networks. A generator and a discriminator constitute a generative adversarial network design, mainly used for image synthesis. The generator iteratively attempts to reconstruct tens of thousands of photos in a dataset. The Discriminator evaluates the Builder’s work after each effort and sends it back to try again, not realizing that the previous rebuild went wrong. The generator has a complete map of the relationships between the points in the dataset at the end of the training.

14. Transformers: Transformer is a revolutionary natural language processing (NLP) technology that powers the autoregressive language model and displays of GPT-3 AI, among others. It solves the problem of sequence transduction, often referred to as “transformation”, which consists of converting input sequences into output sequences. A transformer also receives and processes data in real time rather than in batches, enabling memory persistence that RNN architectures cannot achieve.

15. A priori: In a transactional database, the Apriori algorithm is used to extract frequent itemsets and then construct association rules. The algorithm uses the IF-THEN format to create association rules. This suggests that if event A occurs, then event B is likely to occur with some probability. Google AutoComplete is an example of the Apriori algorithm in action.

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Sherry J. Basler