- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost

- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece

- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting

A

C

D

G

L

M

N

P

R

S

T

X

- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost

- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece

- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting

Supervised learning is the common approach when you have a dataset containing both features (x) and target (y) that you are trying to predict. You apply supervised machine learning algorithms to approximate a function (f) that best maps inputs (x) to an output variable (y).

Because the machine learning algorithm was provided with the correct answer to the problem, the algorithm is able to learn how the other variables relate to the value/answer. This allows you to discover the insights that predict future outcomes based on historical patterns.

Below are two examples of Supervised Machine Learning:

**Regression:**the algorithm produces a numerical target for each example, for instance, how much revenue will be generated from a new marketing campaign.**Classification:**the algorithm labels each example by deciding between two or more different classes. Making decisions between two classes is called a binary classification, for instance, predicting if a person will default on a loan or not. When a decision has to be made between more than two classes it is known as a multiclass classification.

The importance of machine learning lies in its ability to turn data into actionable insights. It allows businesses to leverage data to better understand and avoid an unwanted outcome, or to increase the desired outcome of a target variable.

- AutoML: Automatic Machine Learning
- Cox Proportional Hazards (CoxPH)
- Deep Learning (Neural Networks)
- Distributed Random Forest (DRF)
- Generalized Linear Model (GLM)
- ModelSelection
- Generalized Additive Models (GAM)
- ANOVA GLM
- Gradient Boosting Machine (GBM)
- Naïve Bayes Classifier
- RuleFit
- Stacked Ensembles
- Support Vector Machine (SVM)
- Distributed Uplift Random Forest (Uplift DRF)
- XGBoost

Follow the Regularized Leader (FTRL)

Isolation Forest

LightGBM

PyTorch Models

PyTorch Grownet Model

Random Forest

RuleFit

TensorFlow

XGBoost

Zero-Inflated Models

For more information, check out our documentation.

Supervised machine learning algorithms allow for the discovery of insights to better understand relationships and patterns within a labeled training data set. A labeled training data set already contains the known value, or answer, for the target variable of each record.

In supervised learning, input data is provided to the model along with the output. In unsupervised learning, only input data is provided to the model.

The goal of supervised learning algorithm is to find a mapping function to map the input variable(x) with the output variable(y).

The main advantage of supervised learning is that it allows you to collect data or produce a data output from the previous experience.

Below is a list of common applications that uses supervised machine learning

- Customer Lifetime Value Modeling
- Churn Modeling
- Dynamic Pricing
- Customer Segmentation
- Image Classification

With supervised learning, input data is provided to the model along with the output. For unsupervised learning, only input data is provided to the model.

In classical supervised models, high-level abstraction of input features is not created. But in deep neural networks, abstractions of input features are formed internally.

An advantage of supervised learning is its ability to collect data or produce a data output from the previous experience. A disadvantage of the model is that decision boundary might be overstrained if your training set doesn't have examples that you want to have in a class.