12 Most Used Machine Learning Algorithms in Python
There are many Machine Learning algorithms that are used to provide machines with the ability to learn or determine the required information or insights from datasets. Here is the list of the top 12 most used Machine Learning algorithms which are extensively used to power ML by using Python.
- Naive Bayes
- Decision Tree
- Random Forest
- Apriori Machine Learning Algorithm
- Logistic Regression
- Linear Regression
- K-nearest Neighbors or KNN
- Support Vector Machines or SVM
- Discreet Hopfield Network
- Back-propagation
- XGBoost
-
Hierarchical Clustering
Let’s discuss all the ML Algorithms used in python in detail.
Naive Bayes
This algorithm is based on Bayes’ theorem and consists of a classification method which functions by assuming that the features inside a class are not affected by other features inside the same class. The algorithm assumes that the features have no relation with each other, even if they are dependent on each other. This algorithm offers a model which works really well with massive datasets.
Decision Tree
It is one of the most popular supervised Machine Learning algorithms out there, being used for both classification and regression problems. Decision tree functions by navigating the complete tree and comparing the features with the help of conditional statements. The decision tree runs on both categorical dependent variables and continuous dependent variables.
Random Forest
It fundamentally represents an ensemble learning method for classification, regression and other problems that function by building a compilation of many decision trees during training time. Random Forest classifies objects based on attributes, and each decision tree is given a class. This algorithm then chooses the class which reports the highest number of trees.
Apriori Machine Learning Algorithm
This algorithm is a categorisation algorithm and is used when one needs to sort massive amounts of data. This algorithm can be given a dataset to generate association rules and then used to track developments of the item sets to build categories. It is an unsupervised ML algorithm that can be used to predict occurrences and for the auto-completion of processes.
Logistic Regression
It can be used to predict independent values such as In or Out, 0 or 1 and Negative or Positive. It is one of the most popular supervised ML algorithms, which uses a logistic function to determine the output as either 1 or 0. This is a classification algorithm that follows a statistical model to determine the probabilities of classes or occurrences.
Linear Regression
It helps in predicting an outcome while observing independent features. This ML algorithm helps establish a linear relationship between independent variables and dependent variables. This fundamentally means that it shows how the dependent variable is affected by the value of the independent variables.
K-nearest Neighbors or KNN
It can analyse the labels of given data points surrounding target data points and make predictions to classify the data points. KNN is used for both regression and classification tasks. It is a supervised learning algorithm that is used to recognise patterns, mine data and detect abnormalities.
Support Vector Machines or SVM
It differentiates various categories in the dataset and then effectively optimises this differential line through vector calculation. This is a supervised ML algorithm that helps in analysing data for regression and classification problems.
Discreet Hopfield Network
It helps create an artificial neural network that stores information and can recall this information from partial input. This algorithm offers machines a recurring behaviour that can be termed auto-associative.
Back-propagation
This algorithm is able to design given functions by altering the weights of the input signals, thus producing the required output signals. This is a supervised learning algorithm that is used for classification and regression. Back-propagation finds the minimum values of error functions through gradient descent or the delta rule technique. This is how the algorithm discovers the required weights that will minimise or neutralise error functions.
XGBoost
It is a gradient-boosting algorithm that is used for various functions such as regression, ranking and classification. This contains the tree learning algorithm and the linear model both, which allows XGBoost to predict events with high accuracy. This algorithm is comparably about 10 times faster than prior gradient boosting techniques.
Hierarchical Clustering
This algorithm follows a method of cluster analysis. It functions by putting similar objects into groups called clusters after developing a cluster tree to represent the data. These groups, also known as nodes, are interconnected with two or more successor groups, and each node is allotted data that is similar in nature.
Also read - Prims and Kruskal Algorithm
Frequently Asked Questions
Which is the best machine learning algorithm used in Python?
Linear Regression can be declared as being the best ML algorithm in Python as it is extensively used for ML and preferred by many ML developers. This supervised ML algorithm is popular and acclaimed for predicting outcomes while observing features. It is capable of running on single variables or multiple variables. It is also relatively less complex and easier to apply; hence, it is preferred by a lot of beginners as well.
What algorithms are used in machine learning in Python?
Popular algorithms in Python for machine learning include Linear Regression, Decision Trees, Random Forest, Support Vector Machines, K-Means Clustering, and Neural Networks.
What are the 4 types of machine learning algorithms?
The four types of machine learning algorithms are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
Which algorithm is best in ML?
The "best" machine learning algorithm depends on the problem domain, data characteristics, and performance metrics. Common choices include Random Forest, Gradient Boosting Machines, and Neural Networks.
Which ML algorithm is faster?
The speed of machine learning algorithms varies based on factors such as dataset size, algorithm complexity, and hardware specifications. Algorithms like Naive Bayes and Linear Regression tend to be faster.
How does ML algorithm work?
ML algorithms work by learning patterns and relationships from data, using techniques such as supervised learning with labeled data, unsupervised learning to find hidden patterns, and reinforcement learning through trial and error.
Conclusion
There are many Machine Learning algorithms that help developers and data scientists build machines and systems that are allowing organisations and companies to accomplish various IT, business and statistical goals. When choosing the right ML algorithm, one must consider the environment that he or she is working in and the immediate as well as the future requirement of the project.
Recommended Reading -
ML projects can demand immediate results or solutions to problems, and many algorithms can take various approaches to the same problem. It also does not depend on languages as most of these will work across the common programming languages.
Algorithms are fundamentally the same across all languages as they all originate from mathematics, so when choosing ML algorithms that can serve the same purpose, it completely depends on the user preference and language of choice.
However, when it is not about solving problems and machines need to be programmed into learning through data, user behaviour and real-time use, fundamentally functioning on its own, specific algorithms are best suited for these purposes.
You can also consider our Machine Learning Course to give your career an edge over others.
85+ registered 