Make an output format #5

> list all my experiments

Here are the MLflow experiments currently available in your workspace:

| Experiment ID | Name                         | Description |
|---------------|------------------------------|-------------|
| 0             | Default                      | — |
| 1             | house_price_prediction       | — |
| 2             | digit_classification         | — |
| 3             | customer_churn_prediction    | — |
| 4             | neural_network_digit_classification | — |

Let me know if you’d like details about any specific experiment (e.g., runs, metrics, parameters) or need help analyzing the results.

> list all the runs in the house price prediction experiment

Here are **all runs** that belong to the **“house_price_prediction”** experiment (experiment ID = `1`):

| # | Run ID | Run Name | Status | Start Time (ms epoch) | End Time (ms epoch) |
|---|--------|----------|--------|-----------------------|---------------------|
| 1 | `6e542dbc97e141899e2afe53a7cad2ca` | `rf_nest_500` | FINISHED | 1 771 301 200 288 | 1 771 301 230 578 |
| 2 | `0f62e088ef6a437db9e9d8d1afcda4df` | `rf_nest_200` | FINISHED | 1 771 301 189 001 | 1 771 301 200 253 |
| 3 | `276077b2398c42939f5384e1f0a358b7` | `rf_nest_100` | FINISHED | 1 771 301 181 492 | 1 771 301 188 969 |
| 4 | `a5c72b6bba9d4c58a91347782d51306e` | `rf_nest_50` | FINISHED | 1 771 301 179 011 | 1 771 301 182 457 |
| 5 | `1c3048c28b0a4931b7699f12ea09ee58` | `rf_nest_10` | FINISHED | 1 771 301 178 068 | 1 771 301 178 974 |
| 6 | `955ad30302b04f8494fb69eefa2b5985` | `ridge_alpha_10.0` | FINISHED | 1 771 301 177 623 | 1 771 301 178 017 |
| 7 | `069b60db38a74ddd8d34ae93626a8866` | `ridge_alpha_1.0` | FINISHED | 1 771 301 177 212 | 1 771 301 177 586 |
| 8 | `8890fe1850da49bbbbb9de512563fbb6` | `ridge_alpha_0.1` | FINISHED | 1 771 301 176 821 | 1 771 301 177 177 |
| 9 | `9d3a5edded5843c5883855dfadd8de3b` | `ridge_alpha_0.01` | FINISHED | 1 771 301 176 445 | 1 771 301 176 789 |
|10 | `7054e7498a374c7185374a2fbc2511ee` | `ridge_alpha_0.001` | FINISHED | 1 771 301 175 869 | 1 771 301 176 408 |
|11 | `7e049cb6c15945909e0dd9665b8f328b` | `rf_depth_None` | FINISHED | 1 771 301 165 262 | 1 771 301 175 823 |
|12 | `55daa49ca8384b649165dae7eeb87c8c` | `rf_depth_20` | FINISHED | 1 771 301 156 198 | 1 771 301 165 226 |
|13 | `1ba1c4596ba14f01ba65b294bda563ab` | `rf_depth_10` | FINISHED | 1 771 301 149 313 | 1 771 301 156 160 |
|14 | `98a76d0186594323a173474fbed731b3` | `rf_depth_5` | FINISHED | 1 771 301 145 209 | 1 771 301 149 273 |
|15 | `e529e01382ac4744a03c7795b5ac27ca` | `rf_depth_2` | FINISHED | 1 771 301 143 085 | 1 771 301 145 167 |

All runs have completed successfully (`FINISHED`). Let me know if you’d like to see details (parameters, metrics) for any specific run or need a summary of the best-performing run for a particular metric.

> which of the house price prediction experiment runs perform the best?

**Best run for the “house_price_prediction” experiment**

| Run ID | Run Name | Metric Optimized | Value (lower = better) | Key Metrics | Key Parameters |
|--------|----------|------------------|------------------------|-------------|----------------|
| `6e542dbc97e141899e2afe53a7cad2ca` | `rf_nest_500` | **RMSE (minimized)** | *Not directly reported, but the run was selected as the minimum‑RMSE candidate* | • `val_mse`: 0.2947  <br>• `val_r2`: 0.7751  <br>• `baseline_mse`: 1.3107 | • `model_type`: `random_forest`  <br>• `n_estimators`: `500`  <br>• `max_depth`: `10` |

**Why this run is the top performer**

- The query asked for the run that **minimizes RMSE**. The `find_best_run_by_metric` function searched the *house_price_prediction* experiment (ID = 1) for the lowest RMSE and returned this run.
- Although the response includes `val_mse` rather than an explicit `rmse`, the function’s selection logic indicates that this run achieved the lowest RMSE among all runs in the experiment.
- Its validation MSE (0.295) is substantially lower than the baseline MSE (1.311), showing a strong improvement over a naïve baseline.
- The validation R² of **0.775** confirms that the model explains ~77 % of the variance in the validation set, a solid predictive performance for house price regression.

**What you might want to explore next**

1. **Inspect other metrics** (e.g., `val_rmse`, `train_rmse`) to confirm the exact RMSE value.
2. **Compare with alternative models** (e.g., Gradient Boosting, XGBoost) to see if any can beat this RMSE.
3. **Tune hyper‑parameters** further (e.g., deeper trees, more estimators) to push the validation R² higher.
4. **Check generalization** by comparing training vs. validation/test metrics to ensure no over‑fitting.