Input data
| N | Mean | SD | Min | Max | |
|---|---|---|---|---|---|
| age_in_years | 303 | 54.4389439 | 9.0386624 | 29 | 77.0 |
| sex | 303 | 0.6798680 | 0.4672988 | 0 | 1.0 |
| chest_pain_type | 303 | 3.1584158 | 0.9601256 | 1 | 4.0 |
| resting_blood_pressure | 303 | 131.6897690 | 17.5997477 | 94 | 200.0 |
| fasting_blood_sugar | 303 | 0.1485149 | 0.3561979 | 0 | 1.0 |
| electrocardiographic | 303 | 0.9900990 | 0.9949713 | 0 | 2.0 |
| max_heart_rate | 303 | 149.6072607 | 22.8750033 | 71 | 202.0 |
| exercise_induced_angina | 303 | 0.3267327 | 0.4697945 | 0 | 1.0 |
| depression_induced_exercise | 303 | 1.0396040 | 1.1610750 | 0 | 6.2 |
| slope_of_the_peak_exercise | 303 | 1.6006601 | 0.6162261 | 1 | 3.0 |
| num_of_major_vessels | 299 | 0.6722408 | 0.9374383 | 0 | 3.0 |
| thal | 301 | 4.7342193 | 1.9397058 | 3 | 7.0 |
| serum_cholestoral | 303 | 246.6930693 | 51.7769175 | 126 | 564.0 |
Variable for ml response
| diagnosis of heart disease (angiographic disease status) | N |
|---|---|
| 0 | 164 |
| 1 | 139 |
ML task
| properties | target_names | num_col | num_row |
|---|---|---|---|
| twoclass | num | 14 | 303 |
Classification measures on input data
| classif.acc | classif.bacc | classif.ce |
|---|---|---|
| 0.8360656 | 0.8214286 | 0.1639344 |
Random sample of 5 response values
| row_ids | truth | response |
|---|---|---|
| 246 | 1 | 0 |
| 193 | 1 | 1 |
| 11 | 0 | 0 |
| 262 | 1 | 0 |
| 66 | 1 | 1 |
Learner
| task_type | classif |
| predict_type | response |
| scale.robust | 0 |
| classif.ranger.mtry.ratio | 0.0576324502471834 |
| classif.ranger.num.threads | 1 |
| classif.ranger.num.trees | 227 |
| classif.ranger.replace | 0 |
| classif.ranger.sample.fraction | 0.339497257373296 |
Timings
| train | predict |
|---|---|
| 0.049 | 0.03 |
New data for ml response
| row_ids | truth | response |
|---|---|---|
| 1 | NA | 0 |
A more complete example of machine learning
leavesml with mlr3
Notes classification measures
Notes random sample
Notes data source
## Last Page Update Sun Aug 28 09:28:18 2022