Data Science
A/B Testing
A statistical method used to compare two versions of a product or feature to determine which one performs better.
Clustering
An unsupervised learning technique that groups similar data points together based on shared features.
Correlation
A statistical measure that indicates the extent to which two variables fluctuate together.
Cross-Validation
A technique for assessing how the results of a statistical model will generalize to an independent dataset.
Data Cleaning
The process of detecting and correcting (or removing) inaccurate records from a dataset.
Data Mining
The process of discovering patterns and relationships in large datasets using statistical and machine learning techniques.
Descriptive Statistics
Summary statistics that quantitatively describe features of a dataset, such as mean, median, and standard deviation.
Feature Engineering
The process of selecting, modifying, or creating new variables to improve the performance of a machine learning model.
Hypothesis Testing
A method of making decisions or inferences about population parameters based on sample data.
Linear Regression
A predictive modeling technique used to model the relationship between a dependent variable and one or more independent variables.
Model Evaluation
The process of assessing how well a predictive model performs, using metrics like accuracy, precision, recall, and F1-score.
Overfitting
A modeling error that occurs when a model learns noise in the training data rather than the actual patterns, reducing generalizability.
P-Value
A statistical metric used to determine the significance of results obtained in hypothesis testing.
Predictive Analytics
Using historical data, statistical algorithms, and machine learning to identify the likelihood of future outcomes.
Supervised Learning
A type of machine learning where the model is trained on labeled data to make predictions or classifications.
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