[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': "Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:predicted_x1 =  pipeline.predict(train_numeric_X)\nfig = go.Figure()\nfig.add_trace(go.Histogram(\n    x=train_numeric_Y['ConfirmedCases'],\n    histnorm='percent',\n    name='actual discovered', # name used in legend and hover labels\n    xbins=dict( # bins used for histogram\n        start=-4.0,\n        end=3.0,\n        size=0.5\n    ),\n    opacity=0.75\n))\nfig.add_trace(go.Histogram(\n    x=predicted_x1,\n    histnorm='percent',\n    name='predicted discovered',\n    xbins=dict(\n        start=-3.0,\n        end=4,\n        size=0.5\n    ),\n    opacity=0.75\n))\n\nfig.update_layout(\n    title_text='SVR Histogram of ConfirmedCases', # title of plot\n    xaxis_title_text='bins', # xaxis label\n    yaxis_title_text='ConfirmedCases', # yaxis label\n    bargap=0.2, # gap between bars of adjacent location coordinates\n    bargroupgap=0.1 # gap between bars of the same location coordinates\n)\n\nfig.show()"}, {'role': 'assistant', 'content': "['result_visualization']"}]