Free Practice Questions for Scikit-learn Expert Practitioner Certification Certification
Study with 352 exam-style practice questions designed to help you prepare for the Scikit-learn Expert Practitioner Certification.
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Exam Details
Key information about Scikit-learn Expert Practitioner Certification
- Multiple choice
- Ordering
Expert Practitioner
Senior data scientists
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
Domain 1: Machine learning concepts
Subdomain 1.1: Supervised learning and unsupervised (regression, classification, clustering, dimensional reduction)
Supervised learning and unsupervised (regression, classification, clustering, dimensional reduction)
Subdomain 1.2: Types of model families (tree-based, linear, ensemble, neighbors)
Types of model families (tree-based, linear, ensemble, neighbors)
Subdomain 1.3: Loss functions and surrogate loss
Loss functions and surrogate loss
Subdomain 1.4: Splitting criteria in Decision Trees
Splitting criteria in Decision Trees
Subdomain 1.5: Filter, wrapper and embedded methods for feature selection
Filter, wrapper and embedded methods for feature selection
Subdomain 1.6: Calibration (expected calibration error) vs ranking power (ROC AUC / GINI)
Calibration (expected calibration error) vs ranking power (ROC AUC / GINI)
Domain 2: Model building and evaluation
Subdomain 2.1: Create your own estimator
- NearestCentroid - Recommender systems - Transformers
Subdomain 2.2: Metadata routing
Metadata routing
Subdomain 2.3: Calibration plots with CalibrationDisplay and post-calibration with CalibratedClassifierCV
Calibration plots with CalibrationDisplay and post-calibration with CalibratedClassifierCV
Domain 3: Interpretation of results & communication
Subdomain 3.1: Explainability and interpretability
- partial dependence plots: impact non-linear on the target? - permutation importance
Subdomain 3.2: Debugging the methodology
- given a plot, give a diagnostic for the model - identify pitfalls in the modeling process (e.g. Feature selection techniques inside or outside the pipeline) - code comprehension and good practices
Domain 4: Data preprocessing
Subdomain 4.1: Loading parquet datasets
Loading parquet datasets
Subdomain 4.2: Extract information from plots, e.g. decide on which family of models may be the best fit
Extract information from plots, e.g. decide on which family of models may be the best fit
Subdomain 4.3: Data wrangling
- Combining data from multiple sources - Adding new features or derived attributes (e.g. lagged features for time based data)
Domain 5: Model selection and validation
Subdomain 5.1: Performing hyperparameter tuning with proper scoring rules (calibration)
Performing hyperparameter tuning with proper scoring rules (calibration)
Domain 6: Model deployment
Subdomain 6.1: Understanding how to save and load trained models using joblib , pickle or skops.
Understanding how to save and load trained models using joblib , pickle or skops.
Techniques & products