GuidesPredict with your own table

Predict with your own table

Train a model on your own labelled table and check it on rows it hasn't seen. You choose the task and name the target column; Welt doesn't guess either.

Choose classification (Classifier) for discrete labels, or regression (Regressor) for numeric points.

Declare the target#

Pass the name of your label column to fit. fit(train, target="churn") removes the named target from the features before upload. Pass exactly one of y or target.

The original sklearn form fit(X, y) is still supported, including cloning, Pipeline and cross-validation within checked bounds.

# First run Start's real Iris walkthrough to learn the full six-step workflow.
# For your own approved DataFrame, choose its target explicitly:
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
from welt import Classifier

print(data.head())
data.info()
target = "churn"  # Replace with your own recorded label column.
print(data[target].value_counts())
train, test = train_test_split(data, test_size=0.2, random_state=9,
                             stratify=data[target])
query = test.drop(columns=target)
y_test = test[target]
model = Classifier(model="tabicl-v2", random_state=9)
model.fit(train, target=target)
predictions = model.predict(query)
print("Test accuracy:", accuracy_score(y_test, predictions))
print(classification_report(y_test, predictions, zero_division=0))

This continuation expects your approved data DataFrame with a churn label. Start's real research examples are complete and need no local dataset. For numeric targets, use Regressor and regression metrics instead.

Match the schema#

• DataFrame columns must be unique strings.
• Named query columns can arrive in a different order: they are safely aligned to the fitted names. Missing or extra features are rejected.
• Arrays use positional features, so keep their order.
• Feature cells can be scalar numeric, categorical or missing values. Infinite values and nested objects are rejected.
• Targets can't be missing. Regression targets must be finite numeric values.

target= needs a DataFrame. Read a CSV or Parquet file into one before you use it.

Keep evaluation explicit#

Separate training and query rows yourself. Respect the task's qualified limits: Welt does not silently sample your table.

• During cross-validation, preprocessing belongs inside each training fold.
• When you compare models, use the same held-out rows and metric.
• Keep model selection separate from unbiased assessment.

Inspect the result#

predict returns an array in query row order. Classification probabilities follow classes_ order; regression returns mean points in the target's units.

For operation and version provenance, call model.predict_details(query). To inspect pinned fit metadata, call model.metadata().

A new fit creates a new immutable predictor. Reuse the existing one for later queries, or reopen it later. Switching model, configuration or task requires a new fit; model selection explains the task profiles.

Next steps#