# FIT MODEL
model = XGBRegressor(learning_rate=0.1,
n_estimators=100,
max_depth=3,
gamma=0,
objective='reg:squarederror')
model.fit(tf[features].values, tf.nrtg.values)
# FEATURE IMPORTANCE
fimp = []
for n in features:
fimp.append(model.get_booster().get_score(importance_type='gain')[n])
data = pd.DataFrame(data=fimp, index=clusters,
columns=["score"]).sort_values(by="score")
data.plot(kind='barh', edgecolor='black', legend=None)
plt.title('feature importance')
plt.xlim(0, 400)
plt.tight_layout()
# CREATE ALL POSSIBLE FIVE-MAN LINEUPS
lineups = [i for i in it.product(range(0, 6), repeat=8) if sum(i) == 5]
df = pd.DataFrame(data=lineups, columns=features)
df['nrtg'] = model.predict(df[features])
