Get startedPredict Yacht resistance

Static notebook preview · outputs are cleared. Open in Colab or download the notebook to run the cells. Colab setup

python

!pip install "welt-client[parquet]"

Predict Yacht resistance

Run the installation cell first, connect your workspace, then run the remaining cells in order. These are real hosted requests: fitting retains a private training dataset and predictor. No extra helper file is needed. Outputs are deliberately not saved in this source.

python

from welt import Client

with Client() as client:
    client.connect()

We follow six steps: get data → inspect → split → fit → predict → evaluate. Yacht has 308 rows, six numeric features and the outcome Residuary.resistance. Seed9 random246/62 teaching split. Related experiments may make random-split scores optimistic; this is not a source benchmark.

python

from pathlib import Path
from tempfile import mkdtemp
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error, root_mean_squared_error, r2_score
from welt import Client, Regressor

# 1. Get a real table from Welt's research inventory.
dataset_id = "asset-bc7dddd884c5e798c1b2abfc559a36ab"
path = Path(mkdtemp(prefix="welt-yacht-")) / "yacht.parquet"
with Client() as client:
    info = client.research_dataset(dataset_id)
    print("Dataset:", info["name"])
    client.download_research_dataset(dataset_id, path)
data = pd.read_parquet(path)
target = "Residuary.resistance"  # The outcome column in this table.

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# 2. Look at the rows before modelling.
print(data.head())
print("Rows and columns:", data.shape)
data.info()
print(data[target].describe())

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# 3. Set aside test rows before fitting; never train on their answers.
train, test = train_test_split(
    data, test_size=0.2, random_state=9)
query = test.drop(columns=target)
y_test = test[target]
print("Training rows:", len(train), "— Test rows:", len(test))

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# 4. Create a model and train it on the training table.
model = Regressor(model="tabicl-v2", random_state=9)
model.fit(train, target=target)

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# 5. Predict the held-out rows, without passing their target column.
predictions = model.predict(query)
print(pd.DataFrame({"actual": y_test.to_numpy(), "predicted": predictions}).head())

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# 6. Compare predictions with the answers we kept aside.
print("Test MAE:", mean_absolute_error(y_test, predictions))
print("Test RMSE:", root_mean_squared_error(y_test, predictions))
print("Test R²:", r2_score(y_test, predictions))
print("Keep this predictor ID:", model.predictor_id_)

Read the actual versus predicted rows and the metrics above. Scores depend on your held-out split and model version; no minimum score is promised. Keep the test set for assessment rather than repeated tuning.

python

print("Pinned model version:", model.model_version_)
print("Reusable predictor:", model.predictor_id_)

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