Random Forest

Digits dataset from sklearn

import pandas as pd
from sklearn.datasets import load_digits
digits = load_digits()

dir(digits)

['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']

import matplotlib.pyplot as plt

plt.gray() 
for i in range(4):
    plt.matshow(digits.images[i])

<Figure size 640x480 with 0 Axes>

df = pd.DataFrame(digits.data)
df.head()

	2	3	4	5	9	...	54	58	59	60	61	62
0	5.0	13.0	9.0	1.0	0.0	...	0.0	6.0	13.0	10.0	0.0	0.0
1	0.0	12.0	13.0	5.0	0.0	...	0.0	0.0	11.0	16.0	10.0	0.0
2	0.0	4.0	15.0	12.0	0.0	...	5.0	0.0	3.0	11.0	16.0	9.0
3	7.0	15.0	13.0	1.0	8.0	...	9.0	7.0	13.0	13.0	9.0	0.0
4	0.0	1.0	11.0	0.0	0.0	...	0.0	0.0	2.0	16.0	4.0	0.0

5 rows × 64 columns

df['target'] = digits.target

df[0:12]

	2	3	4	5	6	7	9	...	58	59	60	61	62	target
0	5.0	13.0	9.0	1.0	0.0	0.0	0.0	...	6.0	13.0	10.0	0.0	0.0	0
1	0.0	12.0	13.0	5.0	0.0	0.0	0.0	...	0.0	11.0	16.0	10.0	0.0	1
2	0.0	4.0	15.0	12.0	0.0	0.0	0.0	...	0.0	3.0	11.0	16.0	9.0	2
3	7.0	15.0	13.0	1.0	0.0	0.0	8.0	...	7.0	13.0	13.0	9.0	0.0	3
4	0.0	1.0	11.0	0.0	0.0	0.0	0.0	...	0.0	2.0	16.0	4.0	0.0	4
5	12.0	10.0	0.0	0.0	0.0	0.0	0.0	...	9.0	16.0	16.0	10.0	0.0	5
6	0.0	12.0	13.0	0.0	0.0	0.0	0.0	...	1.0	9.0	15.0	11.0	3.0	6
7	7.0	8.0	13.0	16.0	15.0	1.0	0.0	...	13.0	5.0	0.0	0.0	0.0	7
8	9.0	14.0	8.0	1.0	0.0	0.0	0.0	...	11.0	16.0	15.0	11.0	1.0	8
9	11.0	12.0	0.0	0.0	0.0	0.0	2.0	...	9.0	12.0	13.0	3.0	0.0	9
10	1.0	9.0	15.0	11.0	0.0	0.0	0.0	...	1.0	10.0	13.0	3.0	0.0	0
11	0.0	0.0	14.0	13.0	1.0	0.0	0.0	...	0.0	1.0	13.0	16.0	1.0	1

12 rows × 65 columns

Train and the model and prediction

X = df.drop('target',axis='columns')
y = df.target

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)

from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=20)
model.fit(X_train, y_train)

RandomForestClassifier(n_estimators=20)

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?RandomForestClassifier

Init signature:
RandomForestClassifier(
    n_estimators=100,
    *,
    criterion='gini',
    max_depth=None,
    min_samples_split=2,
    min_samples_leaf=1,
    min_weight_fraction_leaf=0.0,
    max_features='sqrt',
    max_leaf_nodes=None,
    min_impurity_decrease=0.0,
    bootstrap=True,
    oob_score=False,
    n_jobs=None,
    random_state=None,
    verbose=0,
    warm_start=False,
    class_weight=None,
    ccp_alpha=0.0,
    max_samples=None,
)
Docstring:     
A random forest classifier.
A random forest is a meta estimator that fits a number of decision tree
classifiers on various sub-samples of the dataset and uses averaging to
improve the predictive accuracy and control over-fitting.
The sub-sample size is controlled with the `max_samples` parameter if
`bootstrap=True` (default), otherwise the whole dataset is used to build
each tree.
For a comparison between tree-based ensemble models see the example
:ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`.
Read more in the :ref:`User Guide <forest>`.
Parameters
----------
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.
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.
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.
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.
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.
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.
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.
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.
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
bootstrap : bool, default=True
    Whether bootstrap samples are used when building trees. If False, the
    whole dataset is used to build each tree.
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`.
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
    <n_jobs>` for more details.
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 <random_state>` for details.
verbose : int, default=0
    Controls the verbosity when fitting and predicting.
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 <warm_start>` and
    :ref:`gradient_boosting_warm_start` for details.
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.
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.
    .. versionadded:: 0.22
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
Attributes
----------
estimator_ : :class:`~sklearn.tree.DecisionTreeClassifier`
    The child estimator template used to create the collection of fitted
    sub-estimators.
    .. versionadded:: 1.2
       `base_estimator_` was renamed to `estimator_`.
base_estimator_ : DecisionTreeClassifier
    The child estimator template used to create the collection of fitted
    sub-estimators.
    .. deprecated:: 1.2
        `base_estimator_` is deprecated and will be removed in 1.4.
        Use `estimator_` instead.
estimators_ : list of DecisionTreeClassifier
    The collection of fitted sub-estimators.
classes_ : ndarray of shape (n_classes,) or a list of such arrays
    The classes labels (single output problem), or a list of arrays of
    class labels (multi-output problem).
n_classes_ : int or list
    The number of classes (single output problem), or a list containing the
    number of classes for each output (multi-output problem).
n_features_in_ : int
    Number of features seen during :term:`fit`.
    .. versionadded:: 0.24
feature_names_in_ : ndarray of shape (`n_features_in_`,)
    Names of features seen during :term:`fit`. Defined only when `X`
    has feature names that are all strings.
    .. versionadded:: 1.0
n_outputs_ : int
    The number of outputs when ``fit`` is performed.
feature_importances_ : ndarray of shape (n_features,)
    The impurity-based feature importances.
    The higher, the more important the feature.
    The importance of a feature is computed as the (normalized)
    total reduction of the criterion brought by that feature.  It is also
    known as the Gini importance.
    Warning: impurity-based feature importances can be misleading for
    high cardinality features (many unique values). See
    :func:`sklearn.inspection.permutation_importance` as an alternative.
oob_score_ : float
    Score of the training dataset obtained using an out-of-bag estimate.
    This attribute exists only when ``oob_score`` is True.
oob_decision_function_ : ndarray of shape (n_samples, n_classes) or             (n_samples, n_classes, n_outputs)
    Decision function computed with out-of-bag estimate on the training
    set. If n_estimators is small it might be possible that a data point
    was never left out during the bootstrap. In this case,
    `oob_decision_function_` might contain NaN. This attribute exists
    only when ``oob_score`` is True.
See Also
--------
sklearn.tree.DecisionTreeClassifier : A decision tree classifier.
sklearn.ensemble.ExtraTreesClassifier : Ensemble of extremely randomized
    tree classifiers.
sklearn.ensemble.HistGradientBoostingClassifier : A Histogram-based Gradient
    Boosting Classification Tree, very fast for big datasets (n_samples >=
    10_000).
Notes
-----
The default values for the parameters controlling the size of the trees
(e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
unpruned trees which can potentially be very large on some data sets. To
reduce memory consumption, the complexity and size of the trees should be
controlled by setting those parameter values.
The features are always randomly permuted at each split. Therefore,
the best found split may vary, even with the same training data,
``max_features=n_features`` and ``bootstrap=False``, if the improvement
of the criterion is identical for several splits enumerated during the
search of the best split. To obtain a deterministic behaviour during
fitting, ``random_state`` has to be fixed.
References
----------
.. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
Examples
--------
>>> from sklearn.ensemble import RandomForestClassifier
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=1000, n_features=4,
...                            n_informative=2, n_redundant=0,
...                            random_state=0, shuffle=False)
>>> clf = RandomForestClassifier(max_depth=2, random_state=0)
>>> clf.fit(X, y)
RandomForestClassifier(...)
>>> print(clf.predict([[0, 0, 0, 0]]))
[1]
File:           ~/mambaforge/envs/cfast/lib/python3.11/site-packages/sklearn/ensemble/_forest.py
Type:           ABCMeta
Subclasses:

model.score(X_test, y_test)

0.9638888888888889

y_predicted = model.predict(X_test)

Confusion Matrix

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_predicted)
cm

array([[37,  0,  0,  0,  0,  0,  0,  0,  0,  0],
       [ 0, 33,  0,  0,  1,  0,  0,  1,  0,  1],
       [ 1,  0, 32,  2,  0,  0,  0,  0,  1,  0],
       [ 0,  0,  0, 35,  0,  0,  0,  0,  0,  0],
       [ 0,  0,  0,  0, 29,  0,  0,  1,  0,  0],
       [ 0,  0,  0,  0,  0, 40,  0,  0,  1,  0],
       [ 0,  0,  0,  0,  0,  0, 33,  0,  0,  0],
       [ 0,  0,  0,  0,  0,  0,  0, 37,  0,  0],
       [ 0,  0,  1,  0,  0,  0,  0,  1, 35,  0],
       [ 0,  0,  0,  1,  0,  0,  0,  0,  1, 36]])

import matplotlib.pyplot as plt
import seaborn as sn
plt.figure(figsize=(10,7))
sn.heatmap(cm, annot=True)
plt.xlabel('Predicted')
plt.ylabel('Truth')

Text(95.72222222222221, 0.5, 'Truth')