ML Methods
Linear Regression Logistic Regression KNN Random Forest

Random Forest Classifier¶

Objective¶

The goal of this notebook is to perform binary classification using a Random Forest model.

This notebook introduces:

  • decision trees
  • ensemble learning
  • robustness and variance reduction

Problem type: Classification

In [1]:
import pandas as pd
import numpy as np

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import (
    confusion_matrix,
    classification_report,
    accuracy_score
)
In [2]:
df = pd.read_csv("../data/classification.csv")
df.head()
Out[2]:
feature_0 feature_1 feature_2 feature_3 feature_4 feature_5 feature_6 feature_7 target
0 0.189853 -2.853724 -0.963144 0.318181 1.284353 -1.228444 -1.074027 -1.831145 0
1 0.999778 1.436418 0.482381 -1.586100 -0.634295 -0.393530 2.602929 -1.119546 1
2 2.832780 -0.761251 -2.159733 -0.235862 -0.383240 -0.322028 -1.734361 1.614779 0
3 -1.937572 0.665805 -1.500188 2.501383 2.006982 0.359476 1.857416 -0.478759 1
4 -1.040299 -1.983650 -4.728988 4.836235 3.758709 -1.968504 -2.065338 0.034714 0

Dataset¶

The dataset contains:

  • numerical features
  • a binary target variable

Random Forest can handle features with different scales, so feature scaling is not required.

In [3]:
X = df.drop("target", axis=1)
y = df["target"]
In [4]:
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

Model Overview¶

Random Forest is an ensemble model composed of many decision trees.

Key ideas:

  • each tree is trained on a random subset of data
  • each split considers a random subset of features
  • final prediction is based on majority voting

This reduces overfitting compared to a single decision tree.

In [5]:
model = RandomForestClassifier(
    n_estimators=100,
    random_state=42
)

model.fit(X_train, y_train)
Out[5]:
RandomForestClassifier(random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Parameters
n_estimators n_estimators: int, default=100

The number of trees in the forest.

.. versionchanged:: 0.22
The default value of ``n_estimators`` changed from 10 to 100
in 0.22. 		100
criterion criterion: {"gini", "entropy", "log_loss"}, default="gini"

The function to measure the quality of a split. Supported criteria are
"gini" for the Gini impurity and "log_loss" and "entropy" both for the
Shannon information gain, see :ref:`tree_mathematical_formulation`.
Note: This parameter is tree-specific. 		'gini'
max_depth max_depth: int, default=None

The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples. 		None
min_samples_split min_samples_split: int or float, default=2

The minimum number of samples required to split an internal node:

- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.

.. versionchanged:: 0.18
Added float values for fractions. 		2
min_samples_leaf min_samples_leaf: int or float, default=1

The minimum number of samples required to be at a leaf node.
A split point at any depth will only be considered if it leaves at
least ``min_samples_leaf`` training samples in each of the left and
right branches. This may have the effect of smoothing the model,
especially in regression.

- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.

.. versionchanged:: 0.18
Added float values for fractions. 		1
min_weight_fraction_leaf min_weight_fraction_leaf: float, default=0.0

The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided. 		0.0
max_features max_features: {"sqrt", "log2", None}, int or float, default="sqrt"

The number of features to consider when looking for the best split:

- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a fraction and
`max(1, int(max_features * n_features_in_))` features are considered at each
split.
- If "sqrt", then `max_features=sqrt(n_features)`.
- If "log2", then `max_features=log2(n_features)`.
- If None, then `max_features=n_features`.

.. versionchanged:: 1.1
The default of `max_features` changed from `"auto"` to `"sqrt"`.

Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features. 		'sqrt'
max_leaf_nodes max_leaf_nodes: int, default=None

Grow trees with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes. 		None
min_impurity_decrease min_impurity_decrease: float, default=0.0

A node will be split if this split induces a decrease of the impurity
greater than or equal to this value.

The weighted impurity decrease equation is the following::

N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)

where ``N`` is the total number of samples, ``N_t`` is the number of
samples at the current node, ``N_t_L`` is the number of samples in the
left child, and ``N_t_R`` is the number of samples in the right child.

``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
if ``sample_weight`` is passed.

.. versionadded:: 0.19 		0.0
bootstrap bootstrap: bool, default=True

Whether bootstrap samples are used when building trees. If False, the
whole dataset is used to build each tree. 		True
oob_score oob_score: bool or callable, default=False

Whether to use out-of-bag samples to estimate the generalization score.
By default, :func:`~sklearn.metrics.accuracy_score` is used.
Provide a callable with signature `metric(y_true, y_pred)` to use a
custom metric. Only available if `bootstrap=True`.

For an illustration of out-of-bag (OOB) error estimation, see the example
:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`. 		False
n_jobs n_jobs: int, default=None

The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,
:meth:`decision_path` and :meth:`apply` are all parallelized over the
trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
context. ``-1`` means using all processors. See :term:`Glossary
` for more details. 		None
random_state random_state: int, RandomState instance or None, default=None

Controls both the randomness of the bootstrapping of the samples used
when building trees (if ``bootstrap=True``) and the sampling of the
features to consider when looking for the best split at each node
(if ``max_features < n_features``).
See :term:`Glossary ` for details. 		42
verbose verbose: int, default=0

Controls the verbosity when fitting and predicting. 		0
warm_start warm_start: bool, default=False

When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest. See :term:`Glossary ` and
:ref:`tree_ensemble_warm_start` for details. 		False
class_weight class_weight: {"balanced", "balanced_subsample"}, dict or list of dicts, default=None

Weights associated with classes in the form ``{class_label: weight}``.
If not given, all classes are supposed to have weight one. For
multi-output problems, a list of dicts can be provided in the same
order as the columns of y.

Note that for multioutput (including multilabel) weights should be
defined for each class of every column in its own dict. For example,
for four-class multilabel classification weights should be
[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
[{1:1}, {2:5}, {3:1}, {4:1}].

The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``

The "balanced_subsample" mode is the same as "balanced" except that
weights are computed based on the bootstrap sample for every tree
grown.

For multi-output, the weights of each column of y will be multiplied.

Note that these weights will be multiplied with sample_weight (passed
through the fit method) if sample_weight is specified. 		None
ccp_alpha ccp_alpha: non-negative float, default=0.0

Complexity parameter used for Minimal Cost-Complexity Pruning. The
subtree with the largest cost complexity that is smaller than
``ccp_alpha`` will be chosen. By default, no pruning is performed. See
:ref:`minimal_cost_complexity_pruning` for details. See
:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`
for an example of such pruning.

.. versionadded:: 0.22 		0.0
max_samples max_samples: int or float, default=None

If bootstrap is True, the number of samples to draw from X
to train each base estimator.

- If None (default), then draw `X.shape[0]` samples.
- If int, then draw `max_samples` samples.
- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,
`max_samples` should be in the interval `(0.0, 1.0]`.

.. versionadded:: 0.22 		None
monotonic_cst monotonic_cst: array-like of int of shape (n_features), default=None

Indicates the monotonicity constraint to enforce on each feature.
- 1: monotonic increase
- 0: no constraint
- -1: monotonic decrease

If monotonic_cst is None, no constraints are applied.

Monotonicity constraints are not supported for:
- multiclass classifications (i.e. when `n_classes > 2`),
- multioutput classifications (i.e. when `n_outputs_ > 1`),
- classifications trained on data with missing values.

The constraints hold over the probability of the positive class.

Read more in the :ref:`User Guide `.

.. versionadded:: 1.4 		None

Prediction¶

Predictions are obtained by aggregating the predictions of all decision trees in the forest.

In [6]:
y_pred = model.predict(X_test)

Evaluation¶

Random Forest is evaluated using standard classification metrics:

  • confusion matrix
  • accuracy
  • precision, recall and F1-score
In [7]:
print("Accuracy:", accuracy_score(y_test, y_pred))
print()
print(confusion_matrix(y_test, y_pred))
print()
print(classification_report(y_test, y_pred))

Accuracy: 0.9083333333333333

[[82  1]
 [10 27]]

              precision    recall  f1-score   support

           0       0.89      0.99      0.94        83
           1       0.96      0.73      0.83        37

    accuracy                           0.91       120
   macro avg       0.93      0.86      0.88       120
weighted avg       0.91      0.91      0.90       120

Feature Importance¶

Random Forest provides an estimate of feature importance based on how much each feature contributes to reducing impurity.

In [8]:
importances = pd.Series(
    model.feature_importances_,
    index=X.columns
).sort_values(ascending=False)

importances
Out[8]:
feature_1    0.176372
feature_4    0.170517
feature_0    0.160568
feature_6    0.154211
feature_3    0.127211
feature_2    0.112260
feature_5    0.049614
feature_7    0.049246
dtype: float64

Interpretation¶

Key characteristics of Random Forest:

  • non-linear model
  • robust to noise
  • handles complex interactions
  • less interpretable than linear models
  • usually strong baseline for classification problems