Questions score
- Min
- 1
- Median
- 6.0
- Max
- 6
- Q1
- 4.0
- Avg
- 5.2
- Q3
- 6.0
MLOps Zoomcamp 2024
Homework 5: Monitoring Statistics
Distribution of scores and reported study time for this homework.
Submissions
253
Median total score
6
Average total score
6
Score distribution
All values are points.
Learning in public score
- Min
- -
- Median
- 0.0
- Max
- 7
- Q1
- 0.0
- Avg
- 0.8
- Q3
- 1.0
Total score
- Min
- 1
- Median
- 6.0
- Max
- 14
- Q1
- 5.0
- Avg
- 6.1
- Q3
- 7.0
Time distribution
All values are hours reported by students.
Lectures
- Min
- 0.0
- Median
- 4.0
- Max
- 56.0
- Q1
- 2.0
- Avg
- 5.7
- Q3
- 6.0
Homework
- Min
- 1.0
- Median
- 3.0
- Max
- 38.0
- Q1
- 2.0
- Avg
- 4.2
- Q3
- 5.0
Question breakdown
Correctness and answer distribution per question.
1. Prepare the dataset
241 / 253 correct (95.3%)
1
72044
4 (1.6%)
2
78537
5 (2.0%)
3
57457
241 (95.3%)
4
54396
2 (0.8%)
2. Metric
253 / 253 correct (100.0%)
| Answer | Count |
|---|---|
| ColumnQuantileMetric | 50 |
| ColumnCorrelationsMetric | 20 |
| DatasetCorrelationsMetric | 17 |
| ColumnSummaryMetric | 15 |
| ColumnDistributionMetric | 12 |
| ColumnQuantileMetric(column_name="fare_amount", quantile=0.5) | 7 |
| DatasetSummaryMetric | 6 |
| ColumnValueRangeMetric | 5 |
| DatasetMissingValuesMetric | 4 |
| ColumnDriftMetric | 4 |
| ColumnValueListMetric | 3 |
| ColumnSummaryMetric(column_name='fare_amount') | 3 |
| ColumnMissingValuesMetric | 3 |
| DataQualityStabilityMetric | 3 |
| DatasetCorrelationsMetric() | 3 |
| ColumnCorrelationsMetric(column_name='fare_amount') | 2 |
| ColumnQuantileMetric(column_name='fare_amount', quantile = 0.5) | 2 |
| DatasetDriftMetric | 2 |
| RegressionQualityMetric | 2 |
| ColumnValuePlot | 2 |
| trip_distance | 2 |
| median | 2 |
| ColumnDriftMetric(column_name="fare_amount") | 2 |
| ColumnQuantileMetric(column_name="fare_amount",quantile=0.5) | 2 |
| ConflictPredictionMetric | 2 |
| ColumnSummaryMetric() | 1 |
| DatasetMissingValuesMetric and RMSE | 1 |
| I choose ColumnSummaryMetric(column_name='trip_distance') | 1 |
| Dataset correlation metric | 1 |
| Standart Deviation for trip_distance: ColumnSummaryMetric(column_name='trip_distance') | 1 |
| ColumnQuantileMetric(quantile=0.5) | 1 |
| DataQualityPreset() | 1 |
| ColumnDriftMetric(column_name='fare_amount'), DatasetDriftMetric(), DatasetMissingValuesMetric(), ColumnQuantileMetric(column_name = 'fare_amount', quantile=0.5) | 1 |
| missing values | 1 |
| quantile | 1 |
| Column Correlations Metric | 1 |
| ColumnQuantileMetric(column_name='fare_amount' , quantile=0.5) | 1 |
| ColumnQuantileMetric(column_name="fare_amount", quantile=0.5) # Median fare amount | 1 |
| Column Quantile Metric - Calculates the defined quantile value and plots the distribution for the given numerical column. | 1 |
| {'metrics':'ColumnQuantileMetric','DatasetDriftMetric','DatasetMissingValuesMetric'} | 1 |
| ColumnValueRangeMetric(column_name="fare_amount", left=8, right=15) | 1 |
| fare_amount column name (using the ColumnQuantileMetric 0.5 | 1 |
| ColumnQuantileMetric( column_name="fare_amount", quantile=0.5 ) | 1 |
| "ColumnCorrelationsMetric" | 1 |
| ColumnQuantileMetric(column_name='fare_amount', quantile=0.5) | 1 |
| ColumnMissingValuesMetric() | 1 |
| ColumnQuantileMetric.column_name | 1 |
| DatasetCorrelationsMetric (Calculates the correlations between all columns in the dataset. Uses: Pearson, Spearman, Kendall, Cramer_V.) | 1 |
| Mean fare_amount | 1 |
| DatasetCorrelationsMetric and columnQuantileMetric | 1 |
| ColumnCorrelationsMetric(column_name="fare_amount") | 1 |
| ColumnDriftMetric for the prediction column | 1 |
| payment_type | 1 |
| ColumnDriftMetric(column_name='prediction') | 1 |
| column_quantile_metric | 1 |
| ColumnQuantileMetric(column_name = "fare_amount", quantile = 0.5) | 1 |
| ColumnCorrelationsMetric(column_name="prediction") | 1 |
| ColumnValueRangeMetric for passenger_count feature | 1 |
| ColumnDistributionMetric() | 1 |
| ColumnQuantileMetric(column_name='fare_amount', quantile=0.5) and Metric DatasetCorrelationsMetric | 1 |
| ColumnQuantileMetric de evidently.metrics | 1 |
| RegressionPreset so that i can get the rmse | 1 |
| The "tip_amount" was the new column selected | 1 |
| ColumnQuantileMetric(column_name='trip_distance', quantile=0.9), ColumnCorrelationsMetric(column_name='prediction') | 1 |
| from evidently.metrics import ColumnQuantileMetric report = Report(metrics=[ ColumnQuantileMetric(column_name='fare_amount', quantile=0.5), DatasetDriftMetric(), DatasetMissingValuesMetric() ]) | 1 |
| ColumnDistributionMetric(column_name='trip_distance'), | 1 |
| I added the DatasetCorrelationsMetric and the ColumnQuantileMetric for fare_amount. | 1 |
| ColumnDriftMetric(column_name='prediction'), ColumnQuantileMetric(column_name="fare_amount", quantile=0.5), DatasetDriftMetric(), DatasetMissingValuesMetric(), DatasetSummaryMetric(), and ConflictPredictionMetric() | 1 |
| quantile=0.5 | 1 |
| ColumnQuantileMetric(column_name=fare_amount, quantile=0.5) | 1 |
| ColumnQuantileMetric (13.5) | 1 |
| DatasetSummaryMetric, number_uniques_by_columns for trip_distance | 1 |
| fare_amount | 1 |
| I chose the total number of missing values in the dataset (DatasetMissingValuesMetric) | 1 |
| ColumnCorrelationsMetric(column_name="trip_distance") | 1 |
| ColumnDistributionMetric on column 'trip_distance' | 1 |
| RegressionPerformanceMetrics | 1 |
| Regression Quality Metric - RMSE | 1 |
| ColumnDriftMetric() | 1 |
| ColumnQuantileMetric(column_name='fare_amount', quantile=0.5), ColumnMissingValuesMetric(column_name='passenger_count') | 1 |
| std_error for majority using RegressionQualityMetric | 1 |
| ColumnQuantileMetric(column_name: str, quantile: float) | 1 |
| ColumnCorrelationsMetric() | 1 |
| DatasetSummaryMetric() | 1 |
| ColumnQuantileMetric(column_name='fare_amount',quantile=0.5) | 1 |
| ColumnQuantileMetric, ColumnDistributionMetric | 1 |
| 13.500000 | 1 |
| Column Quantile Metric | 1 |
| report = Report(metrics=[ ColumnDriftMetric(column_name='prediction'), ColumnQuantileMetric(column_name='fare_amount', quantile=0.5), DatasetDriftMetric(), DatasetMissingValuesMetric(), DatasetCorrelationsMetric() ] ) | 1 |
| (55211, 21) | 1 |
| Prediction drift | 1 |
| ColumnQuantileMetricResult | 1 |
| ColumnQuantileMetric tracks the median (0.5 quantile) of the "fare_amount" column. ColumnValueRangeMetric observes the range of values in the "fare_amount" column, offering insights into the data's variability. | 1 |
| 2 | 1 |
| Interquartile Range | 1 |
| ConflictPredictionMetric (for the current data) | 1 |
| total_amount | 1 |
3. Monitoring
212 / 253 correct (83.8%)
1
10
8 (3.2%)
2
12.5
21 (8.3%)
3
14.2
212 (83.8%)
4
14.8
8 (3.2%)
4. Dashboard
201 / 253 correct (79.4%)
1
project_folder (05-monitoring)
7 (2.8%)
2
project_folder/config (05-monitoring/config)
39 (15.4%)
3
project_folder/dashboards (05-monitoring/dashboards)
201 (79.4%)
4
project_folder/data (05-monitoring/data)
1 (0.4%)
Calculated: 13 October 2024, 05:00