“А это для выгрузок или для ежедневного контроля?”

“Нам вообще-то важен срез по регионам, почему его нет? А по приложениям? Точкам входа в продукт?”

“А где сравнение с прошлым месяцем?”

"А нам еще важно следить за этим, сделаешь парочку графиков?

"Смежникам важно смотреть на это, а давай еще сделаем так: на одной вкладке будет общая информация, а на второй возможность для финансов грепнуть

Метрики перформанса могут влиять и на продуктовые показатели. К такому выводу мы пришли после проведения ухудшающих тестов.

If it helps in the short-term, but hurts Duolingo in the long-term, it’s not right

When we disagree, we test ideas and let the metrics decide.

Learners need to get utility out of whatever we’ve built. Otherwise, we’ve made something that adds more complexity to the app and distracts learners from what they’re here to do.

Every feature we ship must be intuitive, delightful, useful, and polished.

If a feature or screen requires explanation or additional context, it’s not right.

Some of our best features and campaigns have come from asking ridiculous, unlikely questions

Talking about ideas is rarely as effective as building them

We don’t chase blame. We dig in and figure out what happened. This is how we continuously raise the bar—not by being perfect, but by being a little bit better each day.

from sklearn.linear_model import LinearRegression
from sklearn.datasets import make_regression
import pandas as pd

X, y = make_regression(n_samples=1000, n_features=10, noise=10, random_state=42)
feature_names = [f"feature_{i}" for i in range(X.shape[1])]

model = LinearRegression()
model.fit(X, y)

importance = pd.Series(model.coef_, index=feature_names).sort_values(ascending=False)

from catboost import CatBoostRegressor

model = CatBoostRegressor(iterations=200, verbose=False)
model.fit(X, y)

feature_importance = pd.Series(model.get_feature_importance(), index=feature_names)

from sklearn.ensemble import RandomForestRegressor
from sklearn.inspection import permutation_importance
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = RandomForestRegressor(n_estimators=100)
model.fit(X_train, y_train)

perm_importance = permutation_importance(model, X_test, y_test, scoring="r2")
importance = pd.Series(perm_importance.importances_mean, index=feature_names)

import shap

explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)

shap.summary_plot(shap_values, X_test, feature_names=feature_names)

Линейная регрессия подходит для простых зависимостей, деревья и бустинг – для сложных, Permutation Importance – когда нужна честная оценка вклада признаков, а SHAP – для объяснения модели

WITH old_product AS (
    SELECT 
        user_id, 
        COUNT(order_id) AS old_orders, 
        SUM(revenue) AS old_revenue
    FROM orders
    WHERE product_id = 'old_product'
      AND order_date < '2024-01-01'
    GROUP BY user_id
),

new_product AS (
    SELECT 
        user_id, 
        COUNT(order_id) AS new_orders, 
        SUM(revenue) AS new_revenue
    FROM orders
    WHERE product_id = 'new_product'
      AND order_date >= '2024-01-01'
    GROUP BY user_id
)

SELECT 
    o.user_id,
    o.old_orders,
    n.new_orders,
    o.old_revenue,
    n.new_revenue,
    CASE 
        WHEN o.old_orders > 0 AND n.new_orders > 0 AND n.new_revenue < o.old_revenue 
            THEN 'Перешли, но платят меньше'
        WHEN o.old_orders > 0 AND n.new_orders > 0 AND n.new_revenue >= o.old_revenue 
            THEN 'Перешли, но выручка сохранилась'
        WHEN o.old_orders > 0 AND n.new_orders = 0 
            THEN 'Остались на старом'
        WHEN o.old_orders = 0 AND n.new_orders > 0 
            THEN 'Новый клиент'
        ELSE 'Нет активности'
    END AS user_behavior
FROM old_product o
LEFT JOIN new_product n ON o.user_id = n.user_id;