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Track model training with MLflow: Log the ROC curve, as described. Use log_figure and do not save to disk #69

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Track model training with MLflow: Log the ROC curve, as described. Use log_figure and do not save to disk #69

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8 changes: 4 additions & 4 deletions experimentation/Track model training with MLflow.ipynb
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Expand Up @@ -412,7 +412,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, let's try to log an artifact. An artifact can be any file. For example, you can plot the ROC curve and store the plot as an image. The image can be logged as an artifact. \n",
"Finally, let's try to log an artifact. An artifact can be any file. For example, you can plot the ROC curve and log the figure as an artifact. \n",
"\n",
"Run the following cell to log a parameter, metric, and an artifact."
]
Expand Down Expand Up @@ -446,11 +446,11 @@
" plt.xlabel('False Positive Rate')\n",
" plt.ylabel('True Positive Rate')\n",
" plt.title('ROC Curve')\n",
" plt.savefig(\"ROC-Curve.png\")\n",
"\n",
" mlflow.log_param(\"estimator\", \"DecisionTreeClassifier\")\n",
" mlflow.log_metric(\"Accuracy\", acc)"
]
" mlflow.log_metric(\"Accuracy\", acc)\n",
" mlflow.log_figure(fig, \"ROC-Curve.png\")"
],
},
{
"attachments": {},
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