- Linear Learner
- Random Forest
- Decision Tree
- Extra Trees
- Multi Perceptron
Use this gradient boosted trees algorithm to provide an accurate prediction of a target variable by combining the estimate of a set of simpler, weaker models. Additionally, it uses a gradient descent algorithm to minimize the loss when adding new models.
XGBoost minimizes a regularized objective function that combines a convex loss function, based onthe difference between the predicted and target outputs, and a penalty term for model complexity. Thisis also referred to as the regression tree function.
Use Linear Learner algorithm to explore a large number of models and choose the best model thatoptimizes either continuous objectives, such as mean square error, cross entropy loss, absolute error,or discrete objectives suited for classification, such as F1 measure, precision and recall, or accuracy.
When compared with solutions providing a solution to only continuous objectives, the implementationprovides a significant increase in speed over naive hyperparameter optimization techniques.
Use Random Forest algorithm to construct and combine multiple decision trees to provide a moreaccurate prediction. Unlike the decision tree algorithm, the Random Forest algorithm randomly selectsobservations and features and builds several decision trees before averaging the results.
Use Decision Tree algorithm to continuously split the dataset according to a certain parameter, forminga decision tree. The tree has two main entities: decision nodes and leaves. The leaves are the outcomes,and the decision nodes are the points where the data is split.
Extra Trees algorithm implements an estimator that fits many randomized decision trees, also called extra trees, on various sub-samples of the data set. This algorithm uses averaging to improve the predictive accuracy and control over-fitting.
Also known as an MLP (Multilayer Perceptron) is related to the development of “artificial neural networks”, where computing structures are the base of the design of a human brain.
The perceptron algorithm categorizes patterns and groups by finding the linear separation between different objects and patterns that are received through numeric or visual input.
In perceptron, the algorithm takes a set of inputs and returns a set of outputs. Charts are usually used to have these presented for users. In many operating systems languages, a perceptron algorithm can take the form of a “for” or a “while” loop, where each input is processed to generate an output. The results will represent how advanced these types of algorithms can learn from data.
Being one of the most popular unsupervised machine learning techniques, k-means is used on unlabeled numerical data (or data not defined). And due to its simplicity, it helps data scientists when they don’t have the most ordered data set.
The k-means allocates data points to categories, or clusters, by establishing the mean distance between data points.
It then iterates through this technique in order to achieve more precise classifications over time. Once we must first start by classifying our data into k categories, it is necessary that we understand well our data to do this.
Read more in our Machine Learning basics blog