From aa1b6edd1f7e6824ffd8594332511a843aa8c58c Mon Sep 17 00:00:00 2001 From: acidmikk Date: Wed, 6 Dec 2023 01:57:19 +0400 Subject: [PATCH] =?UTF-8?q?edit=20in=20lab=5F3=20(=D0=BF=D1=80=D0=B8=D0=B2?= =?UTF-8?q?=D0=B5=D0=B4=D0=B5=D0=BD=D0=BE=20=D0=B2=20=D1=81=D0=BE=D0=BE?= =?UTF-8?q?=D1=82=D0=B2=D0=B5=D1=82=D1=81=D1=82=D0=B2=D0=B8=D0=B8=20=D1=81?= =?UTF-8?q?=20=D0=B7=D0=B0=D0=B4=D0=B0=D0=BD=D0=B8=D0=B5=D0=BC)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- basharin_sevastyan_lab_3/README.md | 28 ++++--------- basharin_sevastyan_lab_3/accuracy.png | Bin 4192 -> 0 bytes basharin_sevastyan_lab_3/important.png | Bin 22707 -> 0 bytes basharin_sevastyan_lab_3/main.py | 52 ++++++++++--------------- basharin_sevastyan_lab_3/res.png | Bin 0 -> 15086 bytes 5 files changed, 29 insertions(+), 51 deletions(-) delete mode 100644 basharin_sevastyan_lab_3/accuracy.png delete mode 100644 basharin_sevastyan_lab_3/important.png create mode 100644 basharin_sevastyan_lab_3/res.png diff --git a/basharin_sevastyan_lab_3/README.md b/basharin_sevastyan_lab_3/README.md index 58a65b6..df2c3f3 100644 --- a/basharin_sevastyan_lab_3/README.md +++ b/basharin_sevastyan_lab_3/README.md @@ -1,8 +1,8 @@ ## Лабораторная работа 3. Вариант 4. ### Задание -Выполнить ранжирование признаков и решить с помощью библиотечной реализации дерева решений -задачу классификации на 99% данных из курсовой работы. Проверить -работу модели на оставшемся проценте, сделать вывод. +По данным из таблицы реши задачу классификации (с помощью дерева решений) на 99% ваших данных. +Задачу классификации определить необходимо самостоятельно. +Проверьте работу модели на оставшемся проценте, сделайте вывод. Модель: - Дерево решений `DecisionTreeClassifier`. @@ -65,21 +65,8 @@ data['Transmission Type'] = label_encoder.fit_transform(data['Transmission Type' data['Registration Status'] = label_encoder.fit_transform(data['Registration Status']) ``` -#### Выявление значимых параметров -```python -# Оценка важности признаков -feature_importances = clf.feature_importances_ -feature_importance_df = pd.DataFrame({'Feature': X_train.columns, 'Importance': feature_importances}) -feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False) -``` - -#### Решение задачи кластеризации на полном наборе признаков -Чтобы решить задачу кластеризации моделью `DecisionTreeClassifier`, воспользуемся методом `.predict()`. -```python -clf = DecisionTreeClassifier(max_depth=5, random_state=42) -clf.fit(X_train, y_train) -y_pred = clf.predict(X_test) -``` +#### Формулировка задачи +Предсказать статус регистрации автомобиля (Registration Status) на основе других параметров. #### Оценка эффективности Для оценки точности модели будем использовать встроенный инструмент `accuracy_score`: @@ -88,6 +75,7 @@ accuracy = accuracy_score(y_test, y_pred) ``` #### Результаты -![](accuracy.png "Точность") +![](res.png "Точность") -![](important.png "Важность признаков") +### Вывод +Алгоритм показал высокую точность. 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