SenecaCloudG4/Project2

Model Trained Using AutoTrain

• Problem type: Tabular regression

Validation Metrics

• r2: 0.7770511763473569
• mse: 7850730654540.005
• mae: 1734575.7588461537
• rmse: 2801915.5330844657
• rmsle: 0.23713967369435024
• loss: 2801915.5330844657

Best Params

• learning_rate: 0.02229837095040035
• reg_lambda: 2.510764141176911
• reg_alpha: 0.001531565861357925
• subsample: 0.8214234508684097
• colsample_bytree: 0.3555990037002663
• max_depth: 5
• early_stopping_rounds: 355
• n_estimators: 20000
• eval_metric: rmse

Usage

import json
import joblib
import pandas as pd

model = joblib.load('model.joblib')
config = json.load(open('config.json'))

features = config['features']

# data = pd.read_csv("data.csv")
data = data[features]

predictions = model.predict(data)  # or model.predict_proba(data)

# predictions can be converted to original labels using label_encoders.pkl