{"version":"1.0","type":"rich","title":"Gist b2f3fc64ccdc87810403f05a16159e6c","description":"This notebook demonstrates a machine learning workflow for predicting reading outcomes from demographic and neuroimaging data. It loads a tab-separated dataset containing variables such as age, gender, cognitive scores, and a diffusion MRI measure (L_SLFp_FA), then builds and evaluates classification models using scikit-learn’s ExtraTreesClassifier and LogisticRegressionCV. The code employs a Pipeline with StandardScaler for preprocessing, and uses StratifiedKFold for cross-validation to assess model performance. Visualizations include a dataframe preview rendered as an HTML table via pandas, with the analysis executed in an inline matplotlib environment using seaborn for styling.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-b2f3fc64ccdc87810403f05a16159e6c?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}