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update samples -> examples
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@rasbt
rasbt committed Jun 14, 2019
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24 changes: 12 additions & 12 deletions ch07/ch07.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -93,7 +93,7 @@
" - [Evaluating and tuning the ensemble classifier](#Evaluating-and-tuning-the-ensemble-classifier)\n",
"- [Bagging – building an ensemble of classifiers from bootstrap samples](#Bagging----Building-an-ensemble-of-classifiers-from-bootstrap-samples)\n",
" - [Bagging in a nutshell](#Bagging-in-a-nutshell)\n",
" - [Applying bagging to classify samples in the Wine dataset](#Applying-bagging-to-classify-samples-in-the-Wine-dataset)\n",
" - [Applying bagging to classify examples in the Wine dataset](#Applying-bagging-to-classify-examples-in-the-Wine-dataset)\n",
"- [Leveraging weak learners via adaptive boosting](#Leveraging-weak-learners-via-adaptive-boosting)\n",
" - [How boosting works](#How-boosting-works)\n",
" - [Applying AdaBoost using scikit-learn](#Applying-AdaBoost-using-scikit-learn)\n",
Expand Down Expand Up @@ -404,10 +404,10 @@
"\n",
" Parameters\n",
" ----------\n",
" X : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
" Matrix of training samples.\n",
" X : {array-like, sparse matrix}, shape = [n_examples, n_features]\n",
" Matrix of training examples.\n",
"\n",
" y : array-like, shape = [n_samples]\n",
" y : array-like, shape = [n_examples]\n",
" Vector of target class labels.\n",
"\n",
" Returns\n",
Expand Down Expand Up @@ -441,12 +441,12 @@
"\n",
" Parameters\n",
" ----------\n",
" X : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
" Matrix of training samples.\n",
" X : {array-like, sparse matrix}, shape = [n_examples, n_features]\n",
" Matrix of training examples.\n",
"\n",
" Returns\n",
" ----------\n",
" maj_vote : array-like, shape = [n_samples]\n",
" maj_vote : array-like, shape = [n_examples]\n",
" Predicted class labels.\n",
" \n",
" \"\"\"\n",
Expand All @@ -472,14 +472,14 @@
"\n",
" Parameters\n",
" ----------\n",
" X : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
" Training vectors, where n_samples is the number of samples and\n",
" X : {array-like, sparse matrix}, shape = [n_examples, n_features]\n",
" Training vectors, where n_examples is the number of examples and\n",
" n_features is the number of features.\n",
"\n",
" Returns\n",
" ----------\n",
" avg_proba : array-like, shape = [n_samples, n_classes]\n",
" Weighted average probability for each class per sample.\n",
" avg_proba : array-like, shape = [n_examples, n_classes]\n",
" Weighted average probability for each class per example.\n",
"\n",
" \"\"\"\n",
" probas = np.asarray([clf.predict_proba(X)\n",
Expand Down Expand Up @@ -1256,7 +1256,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Applying bagging to classify samples in the Wine dataset"
"## Applying bagging to classify examples in the Wine dataset"
]
},
{
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24 changes: 12 additions & 12 deletions ch07/ch07.py
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Original file line number Diff line number Diff line change
Expand Up @@ -56,7 +56,7 @@
# - [Evaluating and tuning the ensemble classifier](#Evaluating-and-tuning-the-ensemble-classifier)
# - [Bagging – building an ensemble of classifiers from bootstrap samples](#Bagging----Building-an-ensemble-of-classifiers-from-bootstrap-samples)
# - [Bagging in a nutshell](#Bagging-in-a-nutshell)
# - [Applying bagging to classify samples in the Wine dataset](#Applying-bagging-to-classify-samples-in-the-Wine-dataset)
# - [Applying bagging to classify examples in the Wine dataset](#Applying-bagging-to-classify-examples-in-the-Wine-dataset)
# - [Leveraging weak learners via adaptive boosting](#Leveraging-weak-learners-via-adaptive-boosting)
# - [How boosting works](#How-boosting-works)
# - [Applying AdaBoost using scikit-learn](#Applying-AdaBoost-using-scikit-learn)
Expand Down Expand Up @@ -188,10 +188,10 @@ def fit(self, X, y):
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Matrix of training samples.
X : {array-like, sparse matrix}, shape = [n_examples, n_features]
Matrix of training examples.
y : array-like, shape = [n_samples]
y : array-like, shape = [n_examples]
Vector of target class labels.
Returns
Expand Down Expand Up @@ -225,12 +225,12 @@ def predict(self, X):
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Matrix of training samples.
X : {array-like, sparse matrix}, shape = [n_examples, n_features]
Matrix of training examples.
Returns
----------
maj_vote : array-like, shape = [n_samples]
maj_vote : array-like, shape = [n_examples]
Predicted class labels.
"""
Expand All @@ -256,14 +256,14 @@ def predict_proba(self, X):
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and
X : {array-like, sparse matrix}, shape = [n_examples, n_features]
Training vectors, where n_examples is the number of examples and
n_features is the number of features.
Returns
----------
avg_proba : array-like, shape = [n_samples, n_classes]
Weighted average probability for each class per sample.
avg_proba : array-like, shape = [n_examples, n_classes]
Weighted average probability for each class per example.
"""
probas = np.asarray([clf.predict_proba(X)
Expand Down Expand Up @@ -547,7 +547,7 @@ def get_params(self, deep=True):



# ## Applying bagging to classify samples in the Wine dataset
# ## Applying bagging to classify examples in the Wine dataset



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