When I use Pycaret predict_model(dt,data=data_unseen) to analyze only one row appears

Question

I use Python pycaret module to analyze big set of data. I did setup, compare_model, create_model correctly, but when I try to use model I created to predict the unseen_date I splite from beginning, there is only one row come, there is supposee 100k row need predict. I do skip the tune part cause it is take too long but I dont think thats the reason

TSLASAMPLE = TSLA.sample(frac=0.8)
data_unseen  = TSLA.drop(TSLASAMPLE.index)
TSLASAMPLE.reset_index(drop=True, inplace=True)
data_unseen .reset_index(drop=True, inplace=True)
TSLAinput = setup(data = TSLASAMPLE, target= 'prtPrice', use_gpu=True,html=False,silent=True)
dt = create_model('dt')
prediction = predict_model(dt,data=data_unseen)

output:

Model   MAE MSE RMSE    R2  RMSLE   MAPE
0   Decision Tree Regressor 0.1842  1.8393  1.3562  0.9996  0.0303  0.0082

Answer 1

This is expected. The results (1 row) that you see are the metrics on the unseen data. The actual predictions are in your prediction variable.