Build a model using the example Python and R scripts. Everything you need to get started in one package.
#!/usr/bin/env python """ Example classifier on Numerai data using a xgboost regression. """ import pandas as pd from xgboost import XGBRegressor # training data contains features and targets training_data = pd.read_csv("numerai_training_data.csv").set_index("id") # tournament data contains features only tournament_data = pd.read_csv("numerai_tournament_data.csv").set_index("id") feature_names = [f for f in training_data.columns if "feature" in f] # train a model to make predictions on tournament data model = XGBRegressor(max_depth=5, learning_rate=0.01, \ n_estimators=2000, colsample_bytree=0.1) model.fit(training_data[feature_names], training_data["target"]) # submit predictions to numer.ai predictions = model.predict(tournament_data[feature_names]) predictions.to_csv("predictions.csv")
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Note: Numerai runs a quantitative global equity market neutral hedge fund which is unsuitable for most investors. The fund is designed for institutional investors though some high net worth accredited individuals may qualify.