Fig. 5

Random Forest machine learning models accurately predict mouse genotype using behavioural and neurodevelopmental outcomes. Behavioural outcomes, neurodevelopmental outcomes, sex, and treatment (P3T and P9T) were input as predictor variables. A Confusion matrix represented as proportion of prediction from the total observations across 10 validation sets for each genotype. B Variable importance of predictor variables for genotype as measured by increased Gini index when included in the model. C Density plots of behavioural and neurodevelopmental outcomes for each mouse genotype P3T—postnatal day 3 treatment, P9T—postnatal day 9 treatment, EO—eye opening, SOC_EMP_CHAMB—time in empty chamber, SOC_CENT_CHAMB—time in the center chamber, SOC_MOUSE_CHAM—time in mouse chamber, DUR—duration, FREQ—frequency, LAT—latency, USV—ultrasonic vocalization, ICI—intercall interval, OF—open field, Tot—total, RR4—righting reflex postnatal day 4, DURR—duration