Объясни, пожалуйста, шаги, происходящие в коде:
# -*- coding: utf-8 -*-
"""DecisionsTrees_Ensemble.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1Ncq_nEkE6wMJNhG3sIVDXvu3KBJjNGVA

<h1>1. Выбрать подходящую таблицу данных. Построить и визуализировать дерево решений.</h1>
"""

import pandas as pd

# load dataset
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv"
data = pd.read_csv(url, sep=';')

data

data.info()

X = data.drop('quality', axis=1)
y = data['quality'].values

# shuffle data
from sklearn.utils import shuffle
#X, y = shuffle(X, y, random_state=0)

"""<h1>Построить и визуализировать дерево решений.</h1>"""

import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
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)

# Decision Tree Model Training
clf = DecisionTreeClassifier(max_depth=3, random_state=42)
#clf.fit(X, y)
clf.fit(X_train, y_train)

y_pred = clf.predict(X_test)

from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy:.2f}')

# Visualisation
plt.figure(figsize=(12,8))
tree.plot_tree(clf, filled=True, feature_names=X.columns, class_names=[str(i) for i in sorted(set(y))])
plt.title('Decision Tree')
plt.show()

"""<h1>2. Применить метод градиентного бустинга. Вычислить значимость переменных.
</h2>
"""

from sklearn.ensemble import GradientBoostingClassifier
import numpy as np

# Gradient Boosting Model
gb_clf = GradientBoostingClassifier(n_estimators=1000, random_state=12, max_depth=3, learning_rate=0.1)
gb_clf.fit(X_train, y_train)

# Importances of features
importances = gb_clf.feature_importances_
indices = np.argsort(importances)[::-1]

print("Importances of features:")
for f in range(X.shape[1]):
    print(f"{X.columns[indices[f]]}: {importances[indices[f]]}")

print(f"Number of trees: {gb_clf.n_estimators}")

feature_importances = gb_clf.feature_importances_
imortance_df = pd.DataFrame({'feature' : X.columns, 'importance' : feature_importances})
imortance_df = imortance_df.sort_values(by='importance', ascending=False)
imortance_df

plt.figure(figsize=(12, 6))
plt.barh(range(len(importances)), importances[indices], align='center')
plt.yticks(range(len(importances)), [X.columns[i] for i in indices])
plt.xlabel('Importance')
plt.title('Feature Importance')
plt.show()

"""<h1>3. Построить зависимость качества решения (на обучении и скользящем контроле) от числа вершин дерева.
</h1>
"""

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

depths = [i for i in range(1, 11)] # tree depths
train_scores = []
test_scores = []

for depth in depths:
    clf = DecisionTreeClassifier(max_depth=depth, random_state=42)
    clf.fit(X_train, y_train)

    train_scores.append(accuracy_score(y_train, clf.predict(X_train)))
    test_scores.append(accuracy_score(y_test, clf.predict(X_test)))

plt.figure(figsize=(12, 6))
plt.plot(depths, train_scores, label='TRAIN', marker='o')
plt.plot(depths, test_scores, label='TEST', marker='o')
plt.xlabel('Tree Depth')
plt.ylabel('Accuracy')
plt.title('Accuracy dependence on tree depth')
plt.xticks(depths)
plt.ylim(0, 1)
plt.legend()
plt.grid(True)
plt.show()

"""<h1>4. Для метода градиентного бустинга построить зависимость качества решения (на обучении и скользящем контроле) от числа деревьев. Для разной глубины дерева нужно построить несколько зависимостей качества от числа деревьев, чтобы найти оптимальную комбинацию этих параметров.
</h1>
"""

from sklearn.model_selection import KFold
from sklearn.metrics import accuracy_score

depths = [2,3,4,5,6] # trees depths
n_estimators = 500   # number of trees
kf = KFold(n_splits=5, shuffle=True, random_state=42) # KFold for cross_val with 5 folds

results = {}

#print(type(X_train))
#print(type(y_train))

for depth in depths:
    # arrays to store accuracy scores
    train_scores = np.zeros(n_estimators)
    val_scores = np.zeros(n_estimators)

    # cross-val
    for train_idx, val_idx in kf.split(X_train):
        # split data to train and val
        X_tr, X_val = X_train.iloc[train_idx], X_train.iloc[val_idx]
        #y_tr, y_val = y_train.iloc[train_idx], y_train.iloc[val_idx]
        y_tr, y_val = y_train[train_idx], y_train[val_idx]

        # train model of Gradient boosting
        model = GradientBoostingClassifier(n_estimators=n_estimators, learning_rate=0.1, max_depth=depth, random_state=42)
        model.fit(X_tr, y_tr)

        # get predictions
        for i, (y_train_pred, y_val_pred) in enumerate(zip(model.staged_predict(X_tr), model.staged_predict(X_val))):
            train_scores[i] += accuracy_score(y_tr, y_train_pred)
            val_scores[i] += accuracy_score(y_val, y_val_pred)

    # averaging
    val_scores /= kf.get_n_splits()
    train_scores /= kf.get_n_splits()

    results[depth] = {'train' : train_scores, 'val' : val_scores} # save results

plt.figure(figsize=(10,5))
for depth in depths:
    plt.plot(results[depth]['val'], label=f"depth={depth}")
plt.xlabel("Number of trees")
plt.ylabel("Accuracy")
plt.title("Quality dependence on the number of trees at different tree depths (Gradient Boosting)")
plt.grid(True)
plt.legend()
plt.tight_layout()
plt.show()

# max accuracy finding
max_val_score = 0
best_depth = None

for depth, scores in results.items():
    max_score_for_depth = np.max(scores['val'])
    if max_score_for_depth > max_val_score:
        max_val_score = max_score_for_depth
        best_depth = depth

print(f"Max accuracy of validation: {max_val_score:.3f} with depth of tree {best_depth}")

"""<h1>5. Выполнить предыдущий пункт для случайного леса. Сравнить оценку out-of-bag с оценкой по кроссвалидации.
</h1>
"""

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score

n_estimators_range = range(25, 301, 25) # number of trees
oob_scores = []
cv_scores = []

for n in n_estimators_range:
    rf = RandomForestClassifier(n_estimators=n, max_depth=13, oob_score=True, bootstrap=True, n_jobs=1, random_state=42)
    rf.fit(X_train, y_train)

    oob_scores.append(rf.oob_score_)

    cv_score = cross_val_score(rf, X_train, y_train, cv=5, scoring='accuracy').mean()
    cv_scores.append(cv_score)

plt.figure(figsize=(12, 8))
plt.plot(n_estimators_range, oob_scores, label="OOB Score", marker='o')
plt.plot(n_estimators_range, cv_scores, label="Cross-Validation Score", marker='o')
plt.xlabel("Number of trees")
plt.ylabel("Accuracy")
plt.title("Comparing Out of Bag and Cross-Validation Scores")
plt.ylim(0.65, 0.7)
plt.grid(True)
plt.legend()
plt.tight_layout()
plt.show()

# Score storage for different number of trees
results = {'500': [], '300': [], '100': []}

depth_range = range(1, 20)

for depth in depth_range:
    for n in ['500', '300', '100']:
        rf = RandomForestClassifier(n_estimators=int(n), max_depth=depth, random_state=42, n_jobs=1)
        score = cross_val_score(rf, X_train, y_train, scoring="accuracy", cv=5).mean()
        results[n].append(score)

plt.figure(figsize=(12, 6))
for n in results.keys():
    plt.plot(depth_range, results[n], label=f'{n} trees', marker='o')
plt.xlabel("Depth of Trees")
plt.ylabel("Accuracy")
plt.title("Dependence of Random Forest Quality on Tree Depth for Different Number of Trees")
plt.ylim(0.55, 0.7)
plt.grid(True)
plt.legend()
plt.tight_layout()
plt.show()