In this article, I have explained some of the most known algorithms for Machine Learning to help other friends to understand in one minute.
Decision Trees
A decision Tree, aka DTs, is a probability tree-like structure model that continuously splits data to categorize or make predictions based on the previous questions. The model learns the features of the data and answers questions to help you make better decisions.
For example, you can use a decision tree using the answers Yes or No to determine a specific species of bird using data features such as feathers, ability to fly or swim, beak type, etc.
Linear Regression
It is one of the simplest Machine learning algorithms; Linear Regression is used to make predictions on continuous dependent variables with knowledge from independent variables. A dependent variable is an effect whose value depends on changes in the independent variable.
You may remember the line of best fit from school - this is what Linear Regression produces. A simple example is predicting one's weight depending on their height.
Logistic Regression
It is similar to Linear Regression, which is used to make predictions on categorical dependent variables with knowledge of independent variables. A categorical variable has two or more categories. Logistic Regression classifies outputs that can only be between 0 and 1.
For example, you can use Logistic Regression to determine whether a student will be admitted or not to a particular college depending on their grades - either Yes or No, or 0 or 1.
Random Forest
Random Forest is Similar to Decision Trees, a tree-based algorithm. Where Decision Tree consists of one tree, a Random forest uses multiple decision trees for making decisions - a forest of trees.
It combines multiple models to make predictions and can be used in Classification and Regression tasks.
K-Nearest Neighbors
K-Nearest Neighbors uses the statistical knowledge of how close a data point is to another data point and determines if these data points can be grouped. The closeness in the data points reflects the similarities in one another.
For example, if we had a graph with a group of data points close to one another called Group A and another group of data points that were in close proximity to one another called Group B. When we input a new data point, depending on which group the new data point is nearer to - that will be their new classified group.
Support Vector Machines
Like Nearest Neighbor, Support Vector Machines perform classification, regression, and outlier detection tasks. It draws a hyperplane (a straight line) to separate the classes. The data points located on one side of the line will be labelled as Group A, while the points on the other will be labelled as Group B.
For example, when a new data point is inputted, depending on which side of the hyperplane and its location within the margin, it will determine which group the data point belongs to.
k-means Clustering
It is similar to nearest neighbours but uses the method of clustering to group similar items/data points in clusters. The number of groups is referred to as K. You do this by selecting the k value, initializing the centroids, and then selecting the group and finding the average.
For example, if there are 3 clusters present and a new data point is inputted, depending on which cluster it falls in - that is the cluster they belong to.
Naive Bayes
Naive Bayes is based on Bayes’ Theorem, a mathematical formula used to calculate conditional probabilities. Conditional probability is the chance of an outcome occurring, given that another event has also occurred.
It predicts that the probabilities for each class belong to a particular class and that the class with the highest probability is considered the most likely class.
Bagging
Bagging is also known as Bootstrap aggregating and is an ensemble learning technique. Bagging is used in regression and classification models and aims to avoid overfitting data and reduce the variance in the predictions.
Overfitting is when a model fits precisely against its training data - basically not teaching us anything and can be due to various reasons. Random Forest is an example of Bagging.
Boosting
The overall aim of Boosting is to convert weak learners to strong learners. Weak learners are found by applying base learning algorithms which then generate a new weak prediction rule. A random sample of data is inputted into a model and then trained sequentially, aiming to train the weak learners and trying to correct their predecessor
XGBoost, which stands for Extreme Gradient Boosting, is used in Boosting.
Dimensionality Reduction
Dimensionality reduction is used to reduce the number of input variables in the training data by reducing the dimension of your feature set. A model with a high number of features is naturally more complex, leading to a higher chance of overfitting and decreased accuracy.
For example, if you had a dataset with a hundred columns, dimensionality reduction will reduce the number of columns down to twenty. However, you will need Feature Selection to select relevant features and Feature Engineering to generate new features from existing features.
The Principal Component Analysis (PCA) technique is a type of Dimensionality Reduction.
Conclusion
This article aims to help you understand Machine Learning algorithms in the simplest terms.
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