Top 10 Best Machine Learning Algorithms Explained
Top 10 Best Machine Learning Algorithms Explained. All in all, machine learning algorithms are programs that learn from data and use their experience to solve problems autonomously without human intervention or supervision. Well, it consists of a series of learning tasks and logic parameters to solve simple as well as complex difficult tasks.
This article highlights 10 of the best machine learning algorithms in the industry. Thy fall into the different categories of machine learning algorithms: supervised learning, unsupervised learning, and reinforcement learning.
Top 10 Best Machine Learning Algorithms
Outlined below is a list of ten of the best Machine Learning Algorithms used in machine learning today:
1. Linear Regression
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First on the list is Linear Regression. A type of supervised learning based algorithm. All in all, it serves as a tool for investigating as well as forecasting the relationship between two variables, input (x) and output (y).
An example of a linear regression algorithm is a task to arrange a few books of varying sizes on separate shelves based on their corresponding weights. The rule of the task is that you cannot arrange the books on the shelves by weighing them manually. Instead, you need to guess the weight of each book by observing (visual analysis) the sizes and dimensions. So then, they visually make up a combination of visual variables that guide you in choosing your arrangement on the shelves.
Hence, in machine learning, linear regression shows that you establish the relationship between a dependent and independent variable. All is done by fitting them into a regression line. The linear equation mathematically represents this line;
y = mx + c
Where y = the dependent variable,
m = the slope,
x = the independent variable,
and
b = the intercept.
In conclusion, the primary objective of linear regression is to find the best fit line that unveils the relationship between variables x and y.
2. Logistic Regression
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All in all, logistic regression is a kind of regression analysis that uses a dichotomous binary type dependent variable. It describes data, explains the relationship between one dichotomous variable and one or more independent variables.
As a machine learning algorithm, Logistic regression is also used in predictive analysis. Basically, it predicts the categorical dependent variable using a given collection of independent variables. It does this by predicting the output of a categorical dependent variable.
The difference between logistic regression and linear regression mainly lies in how you use them. While linear regression is used to solve problems of regression, logistic regression is used to solve classification problems.
In machine learning, the logistic regression algorithm uses continuous as well as discrete datasets to provide probabilities for and classify new data. It predicts the output of categorical dependent variables as either Yes or No, 0 or 1, true or false, and so on. But it gives the probabilistic values between 0 and 1 instead of the exact values of 0 and 1.
Unlike in linear regression, where we make use of fitting a regression line, a logistic function shaped as an “S” is fitted. The function predicts two maximum values (0 or 1). This “S” curve indicates the possibility that something happens, such as if the cells are cancerous or not.
3. Decision Trees
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Another popular choice in our top 10 Best Machine Learning Algorithms is decision trees. which are visual maps of possible results for a sequence of decisions. Besides, a decision tree makes it possible for companies and individuals to analyse and also compare probable outcomes. In turn, it enables them to make the best possible decision based on visually provided factors and a mathematical construct.
The design of the decision tree algorithm is similar to an actual tree. Starting with a root node known as a decision node which branches into sub-nodes depicting the potential outcomes at each stage. Decision tree algorithms are also capable of using a mathematical construct to determine the best option. Furthermore, with each outcome generated on the tree, more child nodes are created to open up more suggestions for an infinite number of outcomes.
This tree like structure solves classification problems. Therefore, it is perfect for the two step process of classification, which is the learning step and prediction step. In the learning step, training data is fed to the model to develop it, while the prediction step entails using the model to predict the response for the given data.
The aim of using a decision tree is to create a training model that learns simple decision rules. Also use the training data to predict the value or class of the target variable.
There are two types of decision trees based on the type of target variables, namely:
- Categorical Variable Decision Tree which refers to a decision tree with a categorical target variable.
- Continuous Variable Decision Tree which refers to a decision tree with a continuous target variable.
4. SVM Algorithm
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Support Vector Machine algorithm, popularly known as SVM Algorithm, is a classification algorithm. As noted, it entails plotting raw data (points) in an n-dimensional space (where n equals the number of features you have). The value of each feature is then assigned to a unique coordinate to classify the data easily. The data is then split and plotted on a graph using classifiers.
As a linear algorithm, the SVM algorithm is different from other machine learning algorithms. Why? as it solves classification and regression problems using an SVM classifier and an SVM regressor, respectively. However, the SVM classifier is the central core of the SVM Algorithm, which makes the algorithm best for solving classification problems.
As operated by the SVM Classifier, the SVM algorithm works by creating a hyper-lane in an n-dimensional space that separates the data points belonging to different classes. The hyper-plane is selected based on the margin. Because the hyperplane that provides the maximum margin between both classes is considered. In addition, you use support vectors (which are data points close to the hyper-plane that help instruct the hyper-plane) calculate the margins between both classes.
5. Naive Bayes Algorithm
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Comparatively, there is Naive Bayes classifiers. A group of classification algorithms derived from Bayes’ Theorem. Following, it is a family of algorithms based on a shared principle that every classified pair of features is independent of each other.
The Naive Bayes classifiers infer that if a particular feature is present in a class, that feature is unrelated to any other feature present in the class. A Naive Bayes classifier always considers all the properties or features in a class independently. Even if the features are related to each other. This is a distinguishing factor when calculating a specific outcome’s probability.
The Naive Bayes model is simple to build and is very effective for computing massive datasets. Furthermore, the model operates on a simple framework and outperforms many complex classification methods.
6. KNN Algorithm
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K-Nearest Neighbors Algorithm, more commonly known as KNN Algorithm, is a supervised learning classifier. Uses proximity to classify or predict the grouping of an individual data point. Although you use it for classification and regression problems. This is because it works based on the assumption that similar data points are found next to or near each other.
To solve classification problems, the KNN algorithm assigns a class label based on a majority vote. If there are only two categories, majority of greater than 50% is required to draw a conclusion about a class. However, if there are more than two categories, then plurality voting is used as a 50% majority vote is not required to make a class conclusion.
Further, KNN algorithm similarly solves regression problems and classification problems. But in this case, it takes the average k nearest neighbour to derive a prediction about a classification. The significant difference here is that discrete values require classification, while continuous values require regression. However, classification only occurs when you define the distance (such as Euclidean distance).
Also, it is essential to note that the KNN algorithm belongs to a family of learning models known as “lazy learning” models. This means that it only stores the training dataset while undergoing a training stage. In addition, all the computation happens while making a prediction or classification. Well, KNN algorithm depends heavily on memory to store its training data. Therefore, it serves as a memory based or instance based learning method.
7. K-means Clustering
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Next is The K-means clustering algorithm. Also known as the flat clustering algorithm. Henceforth, unsupervised machine learning method that computes and repeats centroids until the optimal centroid is established. It denotes the number of clusters found from the data by the letter “K” in K-means.
This method assigns data points to clusters to ensure the sum of the squared distance between each data point and the centroids is as tiny as possible. Also, the lower the diversity within clusters, the more identical data points form within the same cluster.
K-means clustering is popular for data clustering analysis. It is easy to understand and deploy. In addition, it produces training results quickly. However, compared to other sophisticated clustering techniques, its performance is usually less competitive due to the high variance that can arise from slight variations in the data.
8. Random Forest Algorithm
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Random forest algorithm is a supervised learning algorithm in machine learning derived from decision tree algorithms. Various industries such as finance and e-commerce make use of this algorithm to predict outcomes and behavior.
A random forest is used to solve regression and classification problems. It uses ensemble learning, a technique that integrates various classifiers to solve complicated problems.
The algorithm comprises of many exclusion trees. The “forest” in the algorithm is generated through bootstrap aggregating or bagging. Random forest algorithm also uses the predictions derived by the decision trees to establish the outcome. Therefore, the more the number of decision trees, the more precise the outcome.
Random forest also eliminates the limitations of the decision tree algorithm by reducing the overfitting of datasets and increasing precision. In addition, it only requires a few package configurations to generate predictions.
9. Dimensionality Reduction Algorithm
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Dimensionality reduction is a technique in machine learning and statistics that involves reducing the number of random variables in a problem by deriving a set of principal variables. You execute this process by using various methods that simplify the modelling of complicated problems, eradicate redundancy, and reduce the chances of overfitting in the model. As a result, it causes results that do not belong in the model.
The dimensionality reduction process has two segments — feature selection and feature extraction. On one hand, feature selection entails selecting smaller subsets of features from a set of multiple dimensional data to represent the entire model by wrapping, filtering, or embedding. On the other, feature extraction entails lowering the number of dimensions in a dataset to model variables and perform component analysis.
Methods of dimensionality reduction include:
- Factor Analysis.
- Low Variance Filter.
- High Correlation Filter.
- Backward Feature Elimination.
- Forward Feature Selection.
- Principal Component Analysis (PCA).
- Linear Discriminant Analysis.
- Methods Based on Projections.
- t-Distributed Stochastic Neighbor Embedding (t-SNE).
- UMAP.
- Independent Component Analysis.
- Missing Value Ratio
- Random Forest
Dimensionality reduction is effective in machine learning and data processing when massive datasets are involved. It helps carry out data visualization, compute massive datasets, and analyse big data. In addition, it greatly reduces data computation times and facilitates data compression to enable the data to consume less storage space.
10. Gradient Boosting Algorithm and AdaBoosting Algorithm
Gradient boosting and Adaboost (Adaptive boosting) are ensemble techniques in machine learning that enhance the effectiveness of weak learners. The aim of the boosting algorithm concept is to crack predictors successively, unlike subsequent models, which try to fix the flaws of their predecessors.
Boosting improves the strength of weak learners, a combination of simple models enhanced by multiple simple boosting techniques. Consequently, adaptive boosting and gradient boosting advance the efficiency of these simple models to yield massive performance in the machine learning algorithm.
Although adaptive boosting and gradient boosting algorithms are similar boosting techniques in machine learning, the significant difference is that adaptive boosting works by reassigning the weights to each instance. Higher weights are assigned to incorrectly classified instances and used to train the model. In comparison, gradient boosting computes complex conservation by large residues from the previous iteration to improve the performance of the existing model.
Thank you for reading Top 10 Best Machine Learning Algorithms Explained. We shall conclude this article.
Top 10 Best Machine Learning Algorithms Explained Conclusion
As machine learning advances in range, adoption, and application, more and more algorithms are developed while the already existing ones are constantly improved upon. As a result, the algorithms that drive machine learning functions and operations today are relatively abundant but may need to be more for the tasks and requirements of the future.
In this article, we talked 10 best machine learning algorithms in use today and explained in detail their machine learning types, the types of problems they solve and their working principles.
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