{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%load_ext watermark"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sebastian Raschka 14/01/2015 \n",
"\n",
"CPython 3.4.2\n",
"IPython 2.3.1\n",
"\n",
"scikit-learn 0.15.2\n",
"numpy 1.9.1\n",
"pandas 0.15.2\n"
]
}
],
"source": [
"%watermark -d -a 'Sebastian Raschka' -v -p scikit-learn,numpy,pandas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Implementing a Weighted Majority Rule Ensemble Classifier in scikit-learn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
If you are interested in using the EnsembleClassifier
, please note that it is now also available through scikit learn (>0.17) as VotingClassifier
.
\n", " | w1 | \n", "w2 | \n", "w3 | \n", "mean | \n", "std | \n", "
---|---|---|---|---|---|
2 | \n", "1 | \n", "2 | \n", "1 | \n", "0.953333 | \n", "0.033993 | \n", "
17 | \n", "3 | \n", "1 | \n", "2 | \n", "0.953333 | \n", "0.033993 | \n", "
16 | \n", "3 | \n", "1 | \n", "1 | \n", "0.946667 | \n", "0.045216 | \n", "
20 | \n", "3 | \n", "2 | \n", "2 | \n", "0.946667 | \n", "0.045216 | \n", "
1 | \n", "1 | \n", "1 | \n", "3 | \n", "0.946667 | \n", "0.040000 | \n", "
6 | \n", "1 | \n", "3 | \n", "2 | \n", "0.946667 | \n", "0.033993 | \n", "
7 | \n", "1 | \n", "3 | \n", "3 | \n", "0.946667 | \n", "0.033993 | \n", "
11 | \n", "2 | \n", "2 | \n", "1 | \n", "0.946667 | \n", "0.033993 | \n", "
13 | \n", "2 | \n", "3 | \n", "1 | \n", "0.946667 | \n", "0.033993 | \n", "
14 | \n", "2 | \n", "3 | \n", "2 | \n", "0.946667 | \n", "0.033993 | \n", "
18 | \n", "3 | \n", "1 | \n", "3 | \n", "0.946667 | \n", "0.033993 | \n", "
22 | \n", "3 | \n", "3 | \n", "1 | \n", "0.946667 | \n", "0.033993 | \n", "
23 | \n", "3 | \n", "3 | \n", "2 | \n", "0.946667 | \n", "0.033993 | \n", "
19 | \n", "3 | \n", "2 | \n", "1 | \n", "0.940000 | \n", "0.057349 | \n", "
5 | \n", "1 | \n", "3 | \n", "1 | \n", "0.940000 | \n", "0.044222 | \n", "
8 | \n", "2 | \n", "1 | \n", "1 | \n", "0.940000 | \n", "0.044222 | \n", "
9 | \n", "2 | \n", "1 | \n", "2 | \n", "0.940000 | \n", "0.044222 | \n", "
12 | \n", "2 | \n", "2 | \n", "3 | \n", "0.940000 | \n", "0.044222 | \n", "
21 | \n", "3 | \n", "2 | \n", "3 | \n", "0.940000 | \n", "0.044222 | \n", "
4 | \n", "1 | \n", "2 | \n", "3 | \n", "0.940000 | \n", "0.038873 | \n", "
3 | \n", "1 | \n", "2 | \n", "2 | \n", "0.940000 | \n", "0.032660 | \n", "
10 | \n", "2 | \n", "1 | \n", "3 | \n", "0.940000 | \n", "0.032660 | \n", "
0 | \n", "1 | \n", "1 | \n", "2 | \n", "0.933333 | \n", "0.047140 | \n", "
15 | \n", "2 | \n", "3 | \n", "3 | \n", "0.933333 | \n", "0.047140 | \n", "