What are the different use cases where machine learning algorithms can be used?
The most common use cases of supervised learning are predicting future trends in price, sales, and stock trading. Examples of supervised algorithms include Linear Regression, Logistical Regression, Neural Networks, Decision Trees, Random Forest, Support Vector Machines (SVM), and Naive Bayes.
Which ML algorithm should I use?
SVM is better suited in case of data with large feature space and lesser observations. PCA and feature selection techniques should be used to reduce dimensionality and select important features. Unsupervised Learning algorithms are used when the training data does not have a response variable.
How many types of supervised learning algorithms are there?
Currently, there are about 10 different types of Supervised Learning algorithms that have clear use cases: Please note: the uses cases defined below are not the only use cases for each type of algorithm.
Which is an example of a machine learning algorithm?
Classification or regression model that improves the accuracy of a simple decision tree by generating multiple decision trees and taking a majority vote of them to predict the output, which is a continuous variable and/or a discrete variable for classification. (i) Predict footfall or covers at a restaurant to predict staffing decisions.
What are the use cases of machine learning?
(i) Providing a decision framework for screening new job candidates. Classification or regression technique that uses a multitude of models to come up with a decision but weighs them based on their accuracy in predicting the outcome. (i) Detect fraudulent activity in banking transactions.
When does tr a ining become a complete algorithm?
Step 3: Tr a ining is considered complete when the algorithm reaches an accuracy rate deemed sufficient. After that, the algorithm is applied to a new dataset. Currently, there are about 10 different types of Supervised Learning algorithms that have clear use cases: