Data science
0 of 12 solved
12 questions
- Confusion matrix, precision, recall and F1 from labelsModel evaluation metricsEasyCoding
- Supervised vs unsupervised vs reinforcement learningMachine learning basicsEasyConceptual
- Mean vs median: which should you report, and what does skewness tell you?Descriptive statisticsEasyConceptual
- Mean, median, mode and standard deviation without librariesDescriptive statisticsEasyCoding
- Classify points with k-nearest neighbours (standard library only)k-NN classificationMediumCoding
- Precision or recall: which matters more, and why is accuracy misleading?Evaluation metricsMediumConceptual
- Overfitting, underfitting and the bias–variance trade-offModel generalisationMediumConceptual
- Handling missing values without leaking informationData preprocessingMediumConceptual
- Fill missing values with the median, then min-max scaleData preprocessingMediumCoding
- Pearson correlation coefficient from scratchCorrelationMediumCoding
- Train/validation/test splits and k-fold cross-validationModel validationMediumConceptual
- Correlation vs causation, and what a p-value really meansInference and experimentsHardConceptual