Databricks Certified Machine Learning Associate: key terms

Flashcards for this exam Databricks Certified Machine Learning Associate guide

• AccuracyThe fraction of all predictions that are correct: (TP + TN) / total.Foundational
• Apache SparkAn open-source distributed engine for large-scale data processing with in-memory computation and APIs for SQL, Python, Scala, Java and R.Foundational
• Area Under the Curve (AUC)The area under the ROC curve, summarizing a classifier's ability to rank positives above negatives (0.5 = random, 1.0 = perfect).Intermediate
• AutoMLAutomated machine learning: tools that automate algorithm selection, feature preprocessing and hyperparameter tuning.Foundational
• BaggingBootstrap aggregating: training models in parallel on random samples with replacement and averaging their outputs.Intermediate
• Bayesian OptimizationHyperparameter tuning that builds a probabilistic model of performance to choose the most promising next trial.Advanced
• BoostingAn ensemble method that trains weak learners sequentially, weighting each toward the errors of its predecessors.Intermediate
• Class ImbalanceA dataset where one class is far more frequent than others, which can bias models toward the majority class.Intermediate
• ClassificationA supervised learning task that predicts which discrete category or class an input belongs to.Foundational
• Confusion MatrixA table comparing predicted classes with actual classes, showing true/false positives and negatives.Foundational
• Cross-ValidationA resampling method that trains and evaluates a model on multiple different splits to estimate generalization more reliably.Intermediate
• Curse of DimensionalityThe set of problems that arise as feature count grows: data becomes sparse and distance measures lose meaning.Intermediate
• Data DriftA change in the statistical distribution of input data in production compared with training data.Intermediate
• Data LeakageWhen information unavailable at prediction time leaks into training, producing unrealistically good evaluation results.Intermediate
• Data LineageThe record of where data originated and how it was transformed on its way to a model or report.Intermediate
• Databricks Mosaic AIDatabricks capabilities for building, evaluating, serving and governing ML and generative AI on the lakehouse.Intermediate
• Decision TreeA model that predicts by learning a hierarchy of if-then splits on feature values.Foundational
• Delta LakeAn open-source table format that adds ACID transactions, schema enforcement and evolution, time travel and MERGE to Parquet files.Intermediate
• Dimensionality ReductionTechniques that reduce the number of input features while preserving as much useful information as possible.Intermediate
• Ensemble LearningCombining predictions from multiple models to achieve better performance than any single model.Intermediate
• Experiment TrackingRecording the code, data, parameters, metrics and artifacts of each training run for comparison and reproducibility.Intermediate
• F1 ScoreThe harmonic mean of precision and recall: 2PR / (P + R).Foundational
• FeatureAn individual measurable input variable that a model uses to make predictions.Foundational
• Feature EngineeringCreating, transforming and selecting input variables to improve model performance.Foundational
• Feature ScalingTransforming numeric features to a common range or distribution so no feature dominates due to its units.Foundational
• Feature SelectionChoosing a subset of the most relevant features and discarding redundant or irrelevant ones.Intermediate
• Feature StoreA centralized repository for storing, sharing and serving ML features consistently for training and inference.Intermediate
• Gradient BoostingAn ensemble technique that builds models sequentially, each one correcting the residual errors of the previous ones.Intermediate
• Grid SearchHyperparameter tuning that exhaustively tries every combination from predefined value lists.Intermediate
• HyperparameterA configuration value set before training that controls the learning process, rather than being learned from data.Foundational
• Hyperparameter TuningSearching for the hyperparameter values that give the best validation performance.Intermediate
• K-Means ClusteringAn unsupervised algorithm that partitions data into k clusters by assigning points to the nearest centroid and iteratively updating centroids.Foundational
• K-Nearest Neighbors (k-NN)A non-parametric algorithm that predicts by majority vote or average of the k closest training examples.Intermediate
• L1 RegularizationAdds a penalty proportional to the absolute value of weights, pushing some weights to exactly zero (Lasso).Intermediate
• L2 RegularizationAdds a penalty proportional to the squared weights, shrinking all weights toward zero (Ridge, weight decay).Intermediate
• Lazy EvaluationDeferring execution of transformations until an action requires a result, which lets the engine optimize the whole plan.Intermediate
• Linear RegressionA regression algorithm that models the target as a weighted sum of input features plus an intercept.Foundational
• Logistic RegressionA classification algorithm that applies the sigmoid function to a linear combination of features to output a probability.Foundational
• Machine Learning (ML)A subset of AI in which systems learn patterns from data to make predictions or decisions without being explicitly programmed with rules.Foundational
• Machine Learning Operations (MLOps)Practices and tooling that apply DevOps principles to build, deploy, monitor and maintain ML models reliably in production.Foundational
• Mean Squared Error (MSE)The average of squared differences between predicted and actual values.Foundational
• ML PipelineAn automated, repeatable sequence of steps such as data preparation, training, evaluation and deployment.Intermediate
• MLflowAn open-source platform for experiment tracking, model packaging, model registry and evaluation.Intermediate
• Model MonitoringContinuously tracking a deployed model's inputs, outputs, performance and fairness to detect degradation.Intermediate
• Model RegistryA central catalog that stores model versions with metadata, lineage and approval status for deployment.Intermediate
• One-Hot EncodingRepresenting a categorical variable as multiple binary columns, one per category.Foundational
• PrecisionOf all positive predictions, the fraction that are actually positive: TP / (TP + FP).Foundational
• Principal Component Analysis (PCA)A linear technique that projects data onto orthogonal directions of maximum variance to reduce dimensionality.Intermediate
• PyTorchAn open-source deep learning framework, originally from Meta and now governed by the PyTorch Foundation, known for dynamic computation graphs.Foundational
• Random ForestAn ensemble of decision trees trained on random data samples and feature subsets, averaging their predictions.Intermediate
• RecallOf all actual positives, the fraction the model correctly identifies: TP / (TP + FN). Also called sensitivity or true positive rate.Foundational
• RegressionA supervised learning task that predicts a continuous numeric value.Foundational
• ROC CurveReceiver Operating Characteristic curve: a plot of true positive rate against false positive rate across thresholds.Intermediate
• Root Mean Squared Error (RMSE)The square root of MSE, expressing typical prediction error in the target's own units.Foundational
• Spark CachingPersisting a DataFrame in memory and/or disk with cache() or persist() so that repeated actions reuse it instead of recomputing.Intermediate
• Spark DataFrameA distributed collection of rows organized into named, typed columns, optimized by Spark's Catalyst optimizer.Intermediate
• Supervised LearningMachine learning that trains a model on labeled examples, where each input is paired with the correct output.Foundational
• Support Vector Machine (SVM)A supervised algorithm that finds the hyperplane separating classes with the maximum margin.Intermediate
• Train-Test SplitDividing a dataset into separate subsets for training and evaluation.Foundational
• Unity Catalog (UC)Databricks' unified governance layer for data and AI assets, providing centralized access control, auditing, lineage and discovery across workspaces.Intermediate
• Unsupervised LearningMachine learning that finds structure, groupings or patterns in data that has no labels.Foundational
• XGBoostExtreme Gradient Boosting: a fast, regularized, widely used gradient-boosted decision tree library.Intermediate