A/B Testing
A/B Testing is a method of comparing two versions of a webpage or app against each other to determine which one performs better.
Accuracy
Accuracy is the degree to which a measurement or estimate conforms to the true or accepted value.
Active Learning
Active Learning is a machine learning approach where the algorithm selectively queries the most informative data points to improve its accuracy.
AdaBoost
AdaBoost is a machine learning algorithm that combines multiple weak classifiers to create a strong classifier.
Adagrad
Adagrad is an adaptive learning rate optimization algorithm used in training machine learning models.
Adam Optimization
Adam Optimization is an adaptive learning rate optimization algorithm designed for training deep learning models.
Adjusted R-Squared
Adjusted R-Squared is a statistical measure that indicates how well a regression model fits the data, adjusted for the number of predictors used.
Affinity Propagation
Affinity Propagation is a clustering algorithm that identifies exemplars among data points and forms clusters by exchanging messages between data points.
Alternating Least Squares
Alternating Least Squares is a matrix factorization technique used to predict missing values and build recommendation systems.
Apriori Algorithm
The Apriori Algorithm is an influential data mining technique used to identify frequent itemsets and derive association rules in large datasets.
ARIMA
ARIMA is a statistical analysis model that stands for AutoRegressive Integrated Moving Average, used for time series forecasting.
Association Rule Learning
Association Rule Learning is a machine learning method used to discover interesting relations between variables in large datasets.
AUC Score
AUC Score is a performance measurement for classification problems at various threshold settings, indicating the quality of a model's predictions.
AutoML
AutoML is the process of automating the end-to-end process of applying machine learning to real-world problems.
Bagging
Bagging is an ensemble machine learning technique that improves model accuracy by training multiple instances of the same model on random subsets of the data and aggregating their predictions.
Batch Learning
Batch learning is a type of machine learning where models are trained using a large collection of data all at once.
Batch Size
Batch size is the number of training examples utilized in one iteration during the training of a machine learning model.
Bayesian Inference
Bayesian Inference is a statistical method that updates the probability of a hypothesis based on new evidence.
Bayesian Optimization
Bayesian Optimization is a probabilistic model-based optimization technique used to find the maximum of an unknown function efficiently.
Bias-Variance Tradeoff
Bias-Variance Tradeoff is the balance between the error introduced by the model's assumptions (bias) and the error due to variability in predictions (variance).
Boosting
Boosting is a machine learning ensemble technique that combines multiple weak learners to create a strong predictive model.
Bootstrapping
Bootstrapping is a self-starting process where an entrepreneur funds their business using personal finances or operating revenues rather than external investors.
Calibration Curve
A calibration curve is a graphical representation used to determine the concentration of a substance in an unknown sample by comparing it to a set of standard samples with known concentrations.
CatBoost
CatBoost is a high-performance, open-source gradient boosting library designed to handle categorical features naturally and efficiently.
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