Getting Started

Quickstart

Two complete examples - one classifier, one regressor - to get you running in under two minutes.

Your first classifier

python
from fling import Classifier

model = Classifier(data="titanic.csv", target="Survived")
model.train()
model.evaluate()
● output
[ok] Loaded 891 rows, 12 columns
[ok] Split: 712 training rows, 179 test rows
[ok] Trained: LogisticRegression

Results:
  Accuracy:  81.6%
  In plain English: Your model correctly predicted 'Survived' for 146 out of 179 rows.

Explore further:

python
model.explain()           # see which features mattered most
model.compare()           # rank 5 different algorithms
model.visualize()         # 4-panel figure
model.show_sklearn_code() # see the raw sklearn equivalent

Your first regressor

python
from fling import Regressor

model = Regressor(data="house_prices.csv", target="price")
model.train()
model.evaluate()
● output
[ok] Loaded 1,460 rows, 81 columns
[ok] Trained: RandomForestRegressor (medium dataset)

Results:
  Mean Absolute Error:  19,841
  R² Score:  0.87
  In plain English: Your model explains 87% of the variation in 'price'.

Next steps