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.
Class Imbalance
Class imbalance is a machine learning scenario where the number of instances of one class significantly outnumbers the instances of other classes.
Classification
Classification is the process of organizing data or information into categories based on shared characteristics or attributes.
Clustering
Clustering is a machine learning technique that involves grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other…
Cold Start Problem
The Cold Start Problem is a challenge in recommendation systems where new users or items lack sufficient data for accurate predictions.
Collaborative Filtering
Collaborative Filtering is a recommendation system technique that uses user behavior and preferences to predict and suggest items of interest to users.
Concept Drift
Concept drift is the phenomenon where the statistical properties of a target variable change over time in unforeseen ways.
Conditional Random Field
A Conditional Random Field (CRF) is a statistical modeling method used for structured prediction in machine learning, particularly for sequences and labeling tasks.
Confidence Interval
A confidence interval is a range of values used to estimate the true value of a population parameter, expressed with a certain level of confidence.
Confounding Variable
A confounding variable is an external influence in a study or experiment that can distort the results and suggest a false association between the studied variables.
Confusion Matrix
A Confusion Matrix is a table used to evaluate the performance of a classification algorithm by summarizing correct and incorrect predictions.
Content-Based Filtering
Content-based filtering is a recommendation system method that uses the characteristics of items to suggest similar items to users.
Contextual Bandit
Contextual Bandit is a machine learning algorithm that selects actions based on context to maximize rewards over time.
Convergence
Convergence is the process where different elements or systems come together to form a unified whole.
Cost Function
A cost function is a mathematical formula used to measure the error or "cost" of a model's predictions compared to the actual data.
Covariate Shift
Covariate shift is a scenario in machine learning where the distribution of input data changes between the training phase and the application phase.
Cross-Validation
Cross-validation is a statistical method used to estimate the skill of machine learning models using different subsets of data.
Curse of Dimensionality
Curse of Dimensionality is a phenomenon where the complexity and volume of data increase exponentially with the addition of each new dimension or feature.