gvozdev/ratings-prediction

Model Trained Using AutoTrain

• Problem type: Tabular regression

Validation Metrics

• r2: 0.004852553257630565
• mse: 1.704782407585897
• mae: 1.0301575550030646
• rmse: 1.3056731626199174
• rmsle: 0.1919556417083651
• loss: 1.3056731626199174

Best Params

• learning_rate: 0.16113054215755473
• reg_lambda: 3.3566663737449463e-06
• reg_alpha: 1.999845686956423e-05
• subsample: 0.3521158025399591
• colsample_bytree: 0.1661721364825762
• max_depth: 2
• early_stopping_rounds: 172
• 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