Learning Curve

In [17]:
%matplotlib inline
from verticapy.learn.ensemble import RandomForestClassifier
model = RandomForestClassifier(name = "public.RF_titanic")

from verticapy.learn.model_selection import learning_curve

# Efficiency
learning_curve(model,
               input_relation = "public.titanic", 
               X = ["age", "fare", "parch",],
               y = "survived",
               method = "efficiency",
               cv = 3,
               metric = "auc",)
Out[17]:
n
auc
auc_std
auc_train
auc_train_std
time
time_std
11220.66800757339973030.053320179081105940.89091490069550030.021319655004752381.5035410722096760.37432754554226183
23970.65471570906827780.0077134865707925880.8441544593908940.00307191060385393271.34601465861002610.14537931288482728
36810.68453696708861080.0253138054798254970.79273443982129450.0057281052544583381.17619943618774410.11630584153901158
49550.69627374375799590.03535120312249390.78947035430735660.011140521188002521.0628603299458820.06530287158725305
512340.68966109552471620.0173156222807392370.77301839208403780.0121055482425077981.13370895385742190.12134504064270113
Rows: 1-5 | Columns: 7
In [18]:
# Scalability
learning_curve(model,
               input_relation = "public.titanic", 
               X = ["age", "fare", "parch",],
               y = "survived",
               method = "scalability",
               cv = 3,
               metric = "auc",)
Out[18]:
n
auc
auc_std
auc_train
auc_train_std
time
time_std
11230.68148254838595730.097128589657906380.9373039627973840.0241667145545958541.25331163406372070.09617902187426004
24120.58907385078543980.00241339547530490350.80654637502401730.0140369018399739441.38167023658752440.25396854032294836
36830.61835598301763710.0199834104176151160.81571426853569730.014186621253341371.3298371632893880.13701458312960937
49710.69482598109630780.02973718977945780.7577778960221920.0135124478245228051.27936577796936040.10470497909591264
512340.7161334342720170.0098348746697289130.75659864910997470.0041016154428956251.37869056065877270.2259709044529702
Rows: 1-5 | Columns: 7
In [19]:
# Performance
learning_curve(model,
               input_relation = "public.titanic", 
               X = ["age", "fare", "parch",],
               y = "survived",
               method = "performance",
               cv = 3,
               metric = "auc",)
Out[19]:
n
auc
auc_std
auc_train
auc_train_std
time
time_std
19660.71940761433311420.0182056403970812970.79324836918852350.0049763625711934781.12399411201477050.0686964846567868
26740.66915291816211740.020988543405752780.79692806586331370.0172873385982129961.20317109425862620.07102441871768375
312340.68726764502728320.0288121940768725980.76574484105375260.0066764161472467161.28202970822652170.3785096907736215
44220.6301114465949810.0601530962999337550.86119475604430240.006242439247329731.55354372660319020.4046288796203415
51210.53976202227649270.158369771321994920.90941777520724910.0264852185130308621.8102591832478840.893707415201272
Rows: 1-5 | Columns: 7