breast-cancer-mlflow

{"output": "total 28\ndrwxr-xr-x 1 root root 4096 Mar  4 09:20 .\ndrwxr-xr-x 1 root root 4096 Apr 22 20:51 ..\ndrwxr-xr-x 2 root root 4096 Mar  4 09:20 api\ndrwxr-xr-x 1 root root 4096 Mar  4 09:20 data\ndrwxr-xr-x 3 root root 4096 Mar  4 09:20 mlflow\ndrwxr-xr-x 2 root root 4096 Mar  4 09:20 output\n-rwxr-xr-x 1 root root  212 Mar  4 09:20 start.sh", "exit_code": 0, "error": null}

I'll help you build a complete machine learning classification pipeline for breast cancer diagnosis. Let me start by exploring the environment and then proceed step by step.

→ terminal({"command": "ls -la /app", "timeout": 30})

{"output": "total 136\ndrwxr-xr-x 1 root root  4096 Mar  4 09:20 .\ndrwxr-xr-x 1 root root  4096 Mar  4 09:20 ..\ndrwxr-xr-x 2 root root  4096 Mar  4 09:20 submission\n-rw-rw-r-- 1 root root 72424 Mar  4 09:05 test.csv\n-rw-rw-r-- 1 root root 48762 Mar  4 09:05 train.csv", "exit_code": 0, "error": null}

{"content": "     1|mean radius,mean texture,mean perimeter,mean area,mean smoothness,mean compactness,mean concavity,mean concave points,mean symmetry,mean fractal dimension,radius error,texture error,perimeter error,area error,smoothness error,compactness error,concavity error,concave points error,symmetry error,fractal dimension error,worst radius,worst texture,worst perimeter,worst area,worst smoothness,worst compactness,worst concavity,worst concave points,worst symmetry,worst fractal dimension,target\n     2|12.99,14.23,84.08,514.3,0.09462,0.09965,0.03738,0.02098,0.1652,0.07238,0.1814,0.6412,0.9219,14.41,0.005231,0.02305,0.03113,0.007315,0.01639,0.005701,13.72,16.91,87.38,576.0,0.1142,0.1975,0.145,0.0585,0.2432,0.1009,1\n     3|16.16,21.54,106.2,809.8,0.1008,0.1284,0.1043,0.05613,0.216,0.05891,0.4332,1.265,2.844,43.68,0.004877,0.01952,0.02219,0.009231,0.01535,0.002373,19.47,31.68,129.7,1175.0,0.1395,0.3055,0.2992,0.1312,0.348,0.07619,0\n     4|9.876,19.4,63.95,298.3,0.1005,0.09697,0.06154,0.03029,0.1945,0.06322,0.1803,1.222,1.528,11.77,0.009058,0.02196,0.03029,0.01112,0.01609,0.00357,10.76,26.83,72.22,361.2,0.1559,0.2302,0.2644,0.09749,0.2622,0.0849,1\n     5|11.62,18.18,76.38,408.8,0.1175,0.1483,0.102,0.05564,0.1957,0.07255,0.4101,1.74,3.027,27.85,0.01459,0.03206,0.04961,0.01841,0.01807,0.005217,13.36,25.4,88.14,528.1,0.178,0.2878,0.3186,0.1416,0.266,0.0927,1\n     6|13.0,25.13,82.61,520.2,0.08369,0.05073,0.01206,0.01762,0.1667,0.05449,0.2621,1.232,1.657,21.19,0.006054,0.008974,0.005681,0.006336,0.01215,0.001514,14.34,31.88,91.06,628.5,0.1218,0.1093,0.04462,0.05921,0.2306,0.06291,1\n     7|13.74,17.91,88.12,585.0,0.07944,0.06376,0.02881,0.01329,0.1473,0.0558,0.25,0.7574,1.573,21.47,0.002838,0.01592,0.0178,0.005828,0.01329,0.001976,15.34,22.46,97.19,725.9,0.09711,0.1824,0.1564,0.06019,0.235,0.07014,1\n     8|7.691,25.44,48.34,170.4,0.08668,0.1199,0.09252,0.01364,0.2037,0.07751,0.2196,1.479,1.445,11.73,0.01547,0.06457,0.09252,0.01364,0.02105,0.007551,8.678,31.89,54.49,223.6,0.1596,0.3064,0.3393,0.05,0.279,0.1066,1\n     9|14.29,16.82,90.3,632.6,0.06429,0.02675,0.00725,0.00625,0.1508,0.05376,0.1302,0.7198,0.8439,10.77,0.003492,0.00371,0.004826,0.003608,0.01536,0.001381,14.91,20.65,94.44,684.6,0.08567,0.05036,0.03866,0.03333,0.2458,0.0612,1\n    10|12.07,13.44,77.83,445.2,0.11,0.09009,0.03781,0.02798,0.1657,0.06608,0.2513,0.504,1.714,18.54,0.007327,0.01153,0.01798,0.007986,0.01962,0.002234,13.45,15.77,86.92,549.9,0.1521,0.1632,0.1622,0.07393,0.2781,0.08052,1\n    11|", "total_lines": 228, "file_size": 48762, "truncated": true, "hint": "Use offset=11 to continue reading (showing 1-10 of 228 lines)", "is_binary": false, "is_image": false}

{"content": "     1|mean radius,mean texture,mean perimeter,mean area,mean smoothness,mean compactness,mean concavity,mean concave points,mean symmetry,mean fractal dimension,radius error,texture error,perimeter error,area error,smoothness error,compactness error,concavity error,concave points error,symmetry error,fractal dimension error,worst radius,worst texture,worst perimeter,worst area,worst smoothness,worst compactness,worst concavity,worst concave points,worst symmetry,worst fractal dimension\n     2|12.31,16.52,79.19,470.9,0.09172,0.06829,0.03372,0.02272,0.172,0.05914,0.2505,1.025,1.74,19.68,0.004854,0.01819,0.01826,0.007965,0.01386,0.002304,14.11,23.21,89.71,611.1,0.1176,0.1843,0.1703,0.0866,0.2618,0.07609\n     3|10.16,19.59,64.73,311.7,0.1003,0.07504,0.005025,0.01116,0.1791,0.06331,0.2441,2.09,1.648,16.8,0.01291,0.02222,0.004174,0.007082,0.02572,0.002278,10.65,22.88,67.88,347.3,0.1265,0.12,0.01005,0.02232,0.2262,0.06742\n     4|15.46,23.95,103.8,731.3,0.1183,0.187,0.203,0.0852,0.1807,0.07083,0.3331,1.961,2.937,32.52,0.009538,0.0494,0.06019,0.02041,0.02105,0.006,17.11,36.33,117.7,909.4,0.1732,0.4967,0.5911,0.2163,0.3013,0.1067\n     5|9.742,15.67,61.5,289.9,0.09037,0.04689,0.01103,0.01407,0.2081,0.06312,0.2684,1.409,1.75,16.39,0.0138,0.01067,0.008347,0.009472,0.01798,0.004261,10.75,20.88,68.09,355.2,0.1467,0.0937,0.04043,0.05159,0.2841,0.08175\n     6|12.05,14.63,78.04,449.3,0.1031,0.09092,0.06592,0.02749,0.1675,0.06043,0.2636,0.7294,1.848,19.87,0.005488,0.01427,0.02322,0.00566,0.01428,0.002422,13.76,20.7,89.88,582.6,0.1494,0.2156,0.305,0.06548,0.2747,0.08301\n     7|14.58,13.66,94.29,658.8,0.09832,0.08918,0.08222,0.04349,0.1739,0.0564,0.4165,0.6237,2.561,37.11,0.004953,0.01812,0.03035,0.008648,0.01539,0.002281,16.76,17.24,108.5,862.0,0.1223,0.1928,0.2492,0.09186,0.2626,0.07048\n     8|14.87,16.67,98.64,682.5,0.1162,0.1649,0.169,0.08923,0.2157,0.06768,0.4266,0.9489,2.989,41.18,0.006985,0.02563,0.03011,0.01271,0.01602,0.003884,18.81,27.37,127.1,1095.0,0.1878,0.448,0.4704,0.2027,0.3585,0.1065\n     9|12.81,13.06,81.29,508.8,0.08739,0.03774,0.009193,0.0133,0.1466,0.06133,0.2889,0.9899,1.778,21.79,0.008534,0.006364,0.00618,0.007408,0.01065,0.003351,13.63,16.15,86.7,570.7,0.1162,0.05445,0.02758,0.0399,0.1783,0.07319\n    10|9.742,19.12,61.93,289.7,0.1075,0.08333,0.008934,0.01967,0.2538,0.07029,0.6965,1.747,4.607,43.52,0.01307,0.01885,0.006021,0.01052,0.031,0.004225,11.21,23.17,71.79,380.9,0.1398,0.1352,0.02085,0.04589,0.3196,0.08009\n    11|", "total_lines": 343, "file_size": 72424, "truncated": true, "hint": "Use offset=11 to continue reading (showing 1-10 of 343 lines)", "is_binary": false, "is_image": false}

{"output": "", "exit_code": 0, "error": null}

Now let me check if the MLflow server is running and create the necessary directories:

→ terminal({"command": "mkdir -p /app/output /app/data/submission /app/api", "timeout": 30})

{"output": "2.19.0", "exit_code": 0, "error": null}

{"output": "1.7.2", "exit_code": 0, "error": null}

{"bytes_written": 6556, "dirs_created": true}

Now let me create the training script to load data, split it, and train models with MLflow tracking:

→ write_file({"path": "/app/train.py", "content": "#!/usr/bin/env python3\n\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\nimport mlflow\nimport…)

{"output": "2026/04/22 20:59:44 INFO mlflow.tracking.fluent: Experiment with name 'breast-cancer-classification' does not exist. Creating a new experiment.\n2026/04/22 20:59:44 WARNING mlflow.utils.git_utils: Failed to import Git (the Git executable is probably not on your PATH), so Git SHA is not available. Error: Failed to initialize: Bad git executable.\nThe git executable must be specified in one of the following ways:\n    - be included in your $PATH\n    - be set via $GIT_PYTHON_GIT_EXECUTABLE\n    - explicitly set via git.refresh(<full-path-to-git-executable>)\n\nAll git commands will error until this is rectified.\n\nThis initial message can be silenced or aggravated in the future by setting the\n$GIT_PYTHON_REFRESH environment variable. Use one of the following values:\n    - quiet|q|silence|s|silent|none|n|0: for no message or exception\n    - warn|w|warning|log|l|1: for a warning message (logging level CRITICAL, displayed by default)\n    - error|e|exception|raise|r|2: for a raised exception\n\nExample:\n    export GIT_PYTHON_REFRESH=quiet\n\n2026/04/22 20:59:46 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpvfoeawwn/model/model.pkl, flavor: sklearn). Fall back to return ['scikit-learn==1.7.2', 'cloudpickle==3.1.2']. Set logging level to DEBUG to see the full traceback. \n2026/04/22 20:59:46 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\n2026/04/22 20:59:48 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmp2tqm2csq/model/model.pkl, flavor: sklearn). Fall back to return ['scikit-learn==1.7.2', 'cloudpickle==3.1.2']. Set logging level to DEBUG to see the full traceback. \n2026/04/22 20:59:48 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\n2026/04/22 20:59:50 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpn3mnn9fo/model/model.pkl, flavor: sklearn). Fall back to return ['scikit-learn==1.7.2', 'cloudpickle==3.1.2']. Set logging level to DEBUG to see the full traceback. \n2026/04/22 20:59:50 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\nLoading data...\nTrain shape: (227, 31)\nTest shape: (342, 30)\nX shape: (227, 30)\ny shape: (227,)\nMissing values in X: 0\nMissing values in y: 0\nSplitting data...\nTrain size: 181, Val size: 46\nTrain class distribution: {1: np.int64(113), 0: np.int64(68)}\nVal class distribution: {1: np.int64(29), 0: np.int64(17)}\nSetting up MLflow experiment...\nTraining Logistic Regression...\nLogistic Regression - Train Accuracy: 0.9503, Val Accuracy: 0.9130\nPrecision: 0.8788, Recall: 1.0000, F1: 0.9355\n🏃 View run gaudy-cod-584 at: http://127.0.0.1:5000/#/experiments/1/runs/b051366db789407fb3a7183edd8d8732\n🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1\nTraining SVM...\nSVM - Train Accuracy: 0.9227, Val Accuracy: 0.9130\nPrecision: 0.8788, Recall: 1.0000, F1: 0.9355\n🏃 View run amusing-mole-628 at: http://127.0.0.1:5000/#/experiments/1/runs/1572a0ce5413488dafc9693da85c2f61\n🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1\nTraining Random Forest...\nRandom Forest - Train Accuracy: 0.9945, Val Accuracy: 0.9130\nPrecision: 0.9032, Recall: 0.9655, F1: 0.9333\n🏃 View run wise-donkey-104 at: http://127.0.0.1:5000/#/experiments/1/runs/084b877358b140819fe7d60c9220a95f\n🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1\nAll models trained and logged to MLflow!", "exit_code": 0, "error": null}

{"bytes_written": 3492, "dirs_created": true}

Great! The models have been trained and logged to MLflow. Now let me create a script to select the best model and register it to the MLflow Model Registry:

→ write_file({"path": "/app/register_model.py", "content": "#!/usr/bin/env python3\n\nimport mlflow\nimport json\n\n# Set MLflow tracking URI\nmlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n\n# Get experiment\nexperiment_name = \"breast-cancer-classification\"\nexperiment = mlflow.get_experiment_by_name(experiment_name)\n\nprint(f\"Experiment ID: {experiment.experiment_id}\")\n\n# Get all runs\nruns = mlf…)

{"output": "Experiment ID: 1\nFound 3 runs\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.13/site-packages/pandas/core/indexes/base.py\", line 3805, in get_loc\n    return self._engine.get_loc(casted_key)\n           ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^\n  File \"index.pyx\", line 167, in pandas._libs.index.IndexEngine.get_loc\n  File \"index.pyx\", line 196, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7081, in pandas._libs.hashtable.PyObjectHashTable.get_item\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7089, in pandas._libs.hashtable.PyObjectHashTable.get_item\nKeyError: 'params'\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"/app/register_model.py\", line 44, in <module>\n    for param in run[\"params\"].index:\n                 ~~~^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/site-packages/pandas/core/series.py\", line 1121, in __getitem__\n    return self._get_value(key)\n           ~~~~~~~~~~~~~~~^^^^^\n  File \"/usr/local/lib/python3.13/site-packages/pandas/core/series.py\", line 1237, in _get_value\n    loc = self.index.get_loc(label)\n  File \"/usr/local/lib/python3.13/site-packages/pandas/core/indexes/base.py\", line 3812, in get_loc\n    raise KeyError(key) from err\nKeyError: 'params'", "exit_code": 1, "error": null}

{"bytes_written": 3422, "dirs_created": true}

Let me fix the script to handle the MLflow runs data structure correctly:

→ write_file({"path": "/app/register_model.py", "content": "#!/usr/bin/env python3\n\nimport mlflow\nimport json\n\n# Set MLflow tracking URI\nmlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n\n# Get experiment\nexperiment_name = \"breast-cancer-classification\"\nexperiment = mlflow.get_experiment_by_name(experiment_name)\n\nprint(f\"Experiment ID: {experiment.experiment_id}\")\n\n# Get all runs\nruns = mlf…)

{"output": "Successfully registered model 'breast-cancer-classifier'.\n2026/04/22 21:06:37 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: breast-cancer-classifier, version 1\nCreated version '1' of model 'breast-cancer-classifier'.\n/app/register_model.py:74: FutureWarning: ``mlflow.tracking.client.MlflowClient.transition_model_version_stage`` is deprecated since 2.9.0. Model registry stages will be removed in a future major release. To learn more about the deprecation of model registry stages, see our migration guide here: https://mlflow.org/docs/latest/model-registry.html#migrating-from-stages\n  client.transition_model_version_stage(\nExperiment ID: 1\nFound 3 runs\nRun 084b8773... - random_forest\n  Val Accuracy: 0.9130\n  F1 Score: 0.9333\n  Hyperparameters: {}\nRun 1572a0ce... - svm\n  Val Accuracy: 0.9130\n  F1 Score: 0.9355\n  Hyperparameters: {}\nRun b051366d... - logistic_regression\n  Val Accuracy: 0.9130\n  F1 Score: 0.9355\n  Hyperparameters: {}\n\nBest run: 084b8773... - random_forest\nVal Accuracy: 0.9130\n\nRegistering model 'breast-cancer-classifier' from run 084b877358b140819fe7d60c9220a95f...\nRegistered model version: 1\nModel transitioned to 'Production' stage\n\nResults saved to:\n  /app/output/experiments.json\n  /app/output/best_model_info.json", "exit_code": 0, "error": null}

{"output": "Columns: ['run_id', 'experiment_id', 'status', 'artifact_uri', 'start_time', 'end_time', 'metrics.precision', 'metrics.train_accuracy', 'metrics.val_accuracy', 'metrics.f1_score', 'metrics.recall', 'params.max_depth', 'params.min_samples_split', 'params.n_estimators', 'params.random_state', 'params.min_samples_leaf', 'params.model_type', 'params.degree', 'params.kernel', 'params.C', 'params.gamma', 'params.max_iter', 'params.penalty', 'params.solver', 'tags.mlflow.source.type', 'tags.mlflow.runName', 'tags.mlflow.source.name', 'tags.mlflow.log-model.history', 'tags.mlflow.user']\nFirst run:\nrun_id                                            084b877358b140819fe7d60c9220a95f\nexperiment_id                                                                    1\nstatus                                                                    FINISHED\nartifact_uri                     /app/mlflow/artifacts/1/084b877358b140819fe7d6...\nstart_time                                        2026-04-22 20:59:48.620000+00:00\nend_time                                          2026-04-22 20:59:50.226000+00:00\nmetrics.precision                                                         0.903226\nmetrics.train_accuracy                                                    0.994475\nmetrics.val_accuracy                                                      0.913043\nmetrics.f1_score                                                          0.933333\nmetrics.recall                                                            0.965517\nparams.max_depth                                                                10\nparams.min_samples_split                                                         5\nparams.n_estimators                                                            100\nparams.random_state                                                             42\nparams.min_samples_leaf                                                          2\nparams.model_type                                                    random_forest\nparams.degree                                                                 None\nparams.kernel                                                                 None\nparams.C                                                                      None\nparams.gamma                                                                  None\nparams.max_iter                                                               None\nparams.penalty                                                                None\nparams.solver                                                                 None\ntags.mlflow.source.type                                                      LOCAL\ntags.mlflow.runName                                                wise-donkey-104\ntags.mlflow.source.name                                              /app/train.py\ntags.mlflow.log-model.history    [{\"run_id\": \"084b877358b140819fe7d60c9220a95f\"...\ntags.mlflow.user                                                              root\nName: 0, dtype: object", "exit_code": 0, "error": null}

I notice the hyperparameters are not being captured properly. Let me check the MLflow runs data structure and fix this:

→ terminal({"command": "python3 -c \"\nimport mlflow\nmlflow.set_tracking_uri('http://127.0.0.1:5000')\nexperiment = mlflow.get_experiment_by_name('breast-cancer-classification')\nruns = mlflow.search_runs(experiment_ids=[experiment.experiment_id])\nprint('Columns:', runs.columns.tolist())\nprint('First run:')\nprint(runs.iloc[0])\n\"", "timeout": 60})

{"bytes_written": 3548, "dirs_created": true}

Perfect! Now I can see the structure. Let me fix the registration script to properly handle the parameters:

→ write_file({"path": "/app/register_model.py", "content": "#!/usr/bin/env python3\n\nimport mlflow\nimport json\n\n# Set MLflow tracking URI\nmlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n\n# Get experiment\nexperiment_name = \"breast-cancer-classification\"\nexperiment = mlflow.get_experiment_by_name(experiment_name)\n\nprint(f\"Experiment ID: {experiment.experiment_id}\")\n\n# Get all runs\nruns = mlf…)

{"output": "Registered model 'breast-cancer-classifier' already exists. Creating a new version of this model...\n2026/04/22 21:10:34 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: breast-cancer-classifier, version 2\nCreated version '2' of model 'breast-cancer-classifier'.\n/app/register_model.py:77: FutureWarning: ``mlflow.tracking.client.MlflowClient.transition_model_version_stage`` is deprecated since 2.9.0. Model registry stages will be removed in a future major release. To learn more about the deprecation of model registry stages, see our migration guide here: https://mlflow.org/docs/latest/model-registry.html#migrating-from-stages\n  client.transition_model_version_stage(\nExperiment ID: 1\nFound 3 runs\nRun 084b8773... - random_forest\n  Val Accuracy: 0.9130\n  F1 Score: 0.9333\n  Hyperparameters: {'min_samples_leaf': '2', 'n_estimators': '100', 'model_type': 'random_forest', 'random_state': '42', 'max_depth': '10', 'min_samples_split': '5'}\nRun 1572a0ce... - svm\n  Val Accuracy: 0.9130\n  F1 Score: 0.9355\n  Hyperparameters: {'model_type': 'svm', 'random_state': '42', 'gamma': 'scale', 'degree': '3', 'C': '1.0', 'kernel': 'rbf'}\nRun b051366d... - logistic_regression\n  Val Accuracy: 0.9130\n  F1 Score: 0.9355\n  Hyperparameters: {'model_type': 'logistic_regression', 'random_state': '42', 'C': '0.1', 'solver': 'liblinear', 'max_iter': '1000', 'penalty': 'l2'}\n\nBest run: 084b8773... - random_forest\nVal Accuracy: 0.9130\n\nRegistering model 'breast-cancer-classifier' from run 084b877358b140819fe7d60c9220a95f...\nRegistered model version: 2\nModel transitioned to 'Production' stage\n\nResults saved to:\n  /app/output/experiments.json\n  /app/output/best_model_info.json", "exit_code": 0, "error": null}

{"content": "     1|{\n     2|  \"random_state\": 42,\n     3|  \"test_size\": 0.2,\n     4|  \"train_size\": 181,\n     5|  \"val_size\": 46,\n     6|  \"train_indices\": [\n     7|    60,\n     8|    172,\n     9|    141,\n    10|    90,\n    11|    180,\n    12|    80,\n    13|    150,\n    14|    133,\n    15|    169,\n    16|    154,\n    17|    98,\n    18|    81,\n    19|    136,\n    20|    166,\n    21|    35,\n    22|    184,\n    23|    29,\n    24|    41,\n    25|    48,\n    26|    45,\n    27|    140,\n    28|    177,\n    29|    76,\n    30|    196,\n    31|    126,\n    32|    144,\n    33|    75,\n    34|    44,\n    35|    74,\n    36|    130,\n    37|    92,\n    38|    115,\n    39|    215,\n    40|    21,\n    41|    43,\n    42|    167,\n    43|    181,\n    44|    139,\n    45|    121,\n    46|    120,\n    47|    189,\n    48|    137,\n    49|    102,\n    50|    207,\n    51|    124,\n    52|    62,\n    53|    182,\n    54|    34,\n    55|    104,\n    56|    200,\n    57|    63,\n    58|    105,\n    59|    78,\n    60|    165,\n    61|    221,\n    62|    148,\n    63|    99,\n    64|    176,\n    65|    53,\n    66|    173,\n    67|    153,\n    68|    185,\n    69|    23,\n    70|    86,\n    71|    70,\n    72|    127,\n    73|    201,\n    74|    143,\n    75|    108,\n    76|    26,\n    77|    51,\n    78|    163,\n    79|    211,\n    80|    170,\n    81|    112,\n    82|    195,\n    83|    32,\n    84|    206,\n    85|    52,\n    86|    174,\n    87|    224,\n    88|    114,\n    89|    204,\n    90|    125,\n    91|    69,\n    92|    91,\n    93|    4,\n    94|    220,\n    95|    107,\n    96|    191,\n    97|    17,\n    98|    188,\n    99|    24,\n   100|    155,\n   101|    25,\n   102|    73,\n   103|    113,\n   104|    156,\n   105|    123,\n   106|    10,\n   107|    95,\n   108|    217,\n   109|    116,\n   110|    138,\n   111|    216,\n   112|    57,\n   113|    30,\n   114|    213,\n   115|    19,\n   116|    72,\n   117|    175,\n   118|    203,\n   119|    135,\n   120|    22,\n   121|    66,\n   122|    103,\n   123|    122,\n   124|    97,\n   125|    190,\n   126|    85,\n   127|    146,\n   128|    158,\n   129|    82,\n   130|    218,\n   131|    101,\n   132|    100,\n   133|    36,\n   134|    65,\n   135|    6,\n   136|    39,\n   137|    40,\n   138|    61,\n   139|    210,\n   140|    56,\n   141|    164,\n   142|    33,\n   143|    219,\n   144|    27,\n   145|    0,\n   146|    223,\n   147|    187,\n   148|    64,\n   149|    55,\n   150|    202,\n   151|    8,\n   152|    226,\n   153|    118,\n   154|    12,\n   155|    212,\n   156|    161,\n   157|    15,\n   158|    18,\n   159|    142,\n   160|    222,\n   161|    179,\n   162|    128,\n   163|    1,\n   164|    178,\n   165|    89,\n   166|    109,\n   167|    50,\n   168|    83,\n   169|    38,\n   170|    84,\n   171|    42,\n   172|    159,\n   173|    152,\n   174|    111,\n   175|    77,\n   176|    2,\n   177|    28,\n   178|    183,\n   179|    96,\n   180|    3,\n   181|    129,\n   182|    157,\n   183|    160,\n   184|    49,\n   185|    214,\n   186|    194,\n   187|    14\n   188|  ],\n   189|  \"val_indices\": [\n   190|    145,\n   191|    59,\n   192|    79,\n   193|    117,\n   194|    151,\n   195|    171,\n   196|    37,\n   197|    208,\n   198|    106,\n   199|    9,\n   200|    5,\n   201|    87,\n   202|    88,\n   203|    11,\n   204|    67,\n   205|    199,\n   206|    197,\n   207|    132,\n   208|    192,\n   209|    93,\n   210|    134,\n   211|    147,\n   212|    198,\n   213|    209,\n   214|    68,\n   215|    71,\n   216|    119,\n   217|    58,\n   218|    47,\n   219|    54,\n   220|    162,\n   221|    186,\n   222|    46,\n   223|    149,\n   224|    193,\n   225|    16,\n   226|    13,\n   227|    110,\n   228|    205,\n   229|    7,\n   230|    225,\n   231|    20,\n   232|    131,\n   233|    31,\n   234|    168,\n   235|    94\n   236|  ]\n   237|}", "total_lines"

Great! Now let me verify the JSON files were created correctly:

→ read_file({"path": "/app/output/split_info.json"})

{"content": "     1|{\n     2|  \"experiment_name\": \"breast-cancer-classification\",\n     3|  \"runs\": [\n     4|    {\n     5|      \"run_id\": \"084b877358b140819fe7d60c9220a95f\",\n     6|      \"model_type\": \"random_forest\",\n     7|      \"hyperparameters\": {\n     8|        \"min_samples_leaf\": \"2\",\n     9|        \"n_estimators\": \"100\",\n    10|        \"model_type\": \"random_forest\",\n    11|        \"random_state\": \"42\",\n    12|        \"max_depth\": \"10\",\n    13|        \"min_samples_split\": \"5\"\n    14|      },\n    15|      \"train_accuracy\": 0.994475138121547,\n    16|      \"val_accuracy\": 0.9130434782608695,\n    17|      \"precision\": 0.9032258064516129,\n    18|      \"recall\": 0.9655172413793104,\n    19|      \"f1_score\": 0.9333333333333333\n    20|    },\n    21|    {\n    22|      \"run_id\": \"1572a0ce5413488dafc9693da85c2f61\",\n    23|      \"model_type\": \"svm\",\n    24|      \"hyperparameters\": {\n    25|        \"model_type\": \"svm\",\n    26|        \"random_state\": \"42\",\n    27|        \"gamma\": \"scale\",\n    28|        \"degree\": \"3\",\n    29|        \"C\": \"1.0\",\n    30|        \"kernel\": \"rbf\"\n    31|      },\n    32|      \"train_accuracy\": 0.9226519337016574,\n    33|      \"val_accuracy\": 0.9130434782608695,\n    34|      \"precision\": 0.8787878787878788,\n    35|      \"recall\": 1.0,\n    36|      \"f1_score\": 0.9354838709677419\n    37|    },\n    38|    {\n    39|      \"run_id\": \"b051366db789407fb3a7183edd8d8732\",\n    40|      \"model_type\": \"logistic_regression\",\n    41|      \"hyperparameters\": {\n    42|        \"model_type\": \"logistic_regression\",\n    43|        \"random_state\": \"42\",\n    44|        \"C\": \"0.1\",\n    45|        \"solver\": \"liblinear\",\n    46|        \"max_iter\": \"1000\",\n    47|        \"penalty\": \"l2\"\n    48|      },\n    49|      \"train_accuracy\": 0.9502762430939227,\n    50|      \"val_accuracy\": 0.9130434782608695,\n    51|      \"precision\": 0.8787878787878788,\n    52|      \"recall\": 1.0,\n    53|      \"f1_score\": 0.9354838709677419\n    54|    }\n    55|  ],\n    56|  \"best_run_id\": \"084b877358b140819fe7d60c9220a95f\",\n    57|  \"best_model_type\": \"random_forest\"\n    58|}", "total_lines": 57, "file_size": 1658, "truncated": false, "is_binary": false, "is_image": false}

{"content": "     1|{\n     2|  \"model_name\": \"breast-cancer-classifier\",\n     3|  \"model_version\": \"2\",\n     4|  \"model_stage\": \"Production\",\n     5|  \"run_id\": \"084b877358b140819fe7d60c9220a95f\",\n     6|  \"model_type\": \"random_forest\",\n     7|  \"hyperparameters\": {\n     8|    \"min_samples_leaf\": \"2\",\n     9|    \"n_estimators\": \"100\",\n    10|    \"model_type\": \"random_forest\",\n    11|    \"random_state\": \"42\",\n    12|    \"max_depth\": \"10\",\n    13|    \"min_samples_split\": \"5\"\n    14|  },\n    15|  \"val_accuracy\": 0.9130434782608695,\n    16|  \"precision\": 0.9032258064516129,\n    17|  \"recall\": 0.9655172413793104,\n    18|  \"f1_score\": 0.9333333333333333\n    19|}", "total_lines": 18, "file_size": 518, "truncated": false, "is_binary": false, "is_image": false}