Thanks for reading, @Ariana_Rose! I just replaced the “Benchmarks” link with an updated documentation page that works.
Sample Code presented as follows %load_ext cudf.pandas
%load_ext cuml.accel import sys
from pathlib import Path
sys.path.insert(0, str(Path.cwd().parent / ‘src’))
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
from train import FEATURES, build_features, make_synthetic
Data Preparation
raw = make_synthetic(1_000_000, seed=42)
data = build_features(raw, target=‘fare_amount’)
X_train, X_test, y_train, y_test = train_test_split(
data\[FEATURES\], data\['fare_amount'\], test_size=0.2, random_state=42
)
data.shape model = RandomForestRegressor(n_estimators=200, max_depth=18, n_jobs=-1, random_state=42)
model.fit(X_train, y_train)
pred = model.predict(X_test)
{‘rmse’: mean_squared_error(y_test, pred) ** 0.5, ‘r2’: r2_score(y_test, pred)}
%%cudf.pandas.profile
profiled = build_features(raw, target=‘fare_amount’)
@this code snippet helped me a lot
Nice Sample
Steep learning curve, need a few read to understand. but Thank you.
Nice work @jeffnelson . Thanks for the information
i am new in this field please i want to learn all and i do not know to what to do
How exciting
How Amazing
Thanks
Going through this lab now. What stands out to me is that the real win isn’t just “GPU = faster” — it’s being able to profile the workflow and see exactly where CPU fallbacks are creating bottlenecks. That makes the optimization process much more practical. Excited to apply this to some larger simulation workloads I’m working on.