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Davud 2022-12-22 16:53:25 +00:00
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decision tree classification.ipynb
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@ -24,7 +24,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Constructing a model"
"# Constructing a model (algorithm)"
]
},
{
@ -309,10 +309,17 @@
"\n",
"This dataset became a typical test case for many statistical classification techniques in machine learning such as support vector machines\n",
"\n",
"**Reference**\n",
"\n",
" R. A. Fisher (1936). \"The use of multiple measurements in taxonomic problems\". Annals of Eugenics. 7 (2): 179–188.\n",
" \n",
" https://en.wikipedia.org/wiki/Iris_flower_data_set\n",
"\n",
"**Content**\n",
"\n",
"The dataset contains a set of 150 records under 5 attributes - Petal Length, Petal Width, Sepal Length, Sepal width and Class(Species).\n",
"\n",
"---\n",
"So, our objective here is to predict the class that is the specie of the iris flower, given it's features which are:\n",
"1. sepal_length\n",
"2. sepal width\n",
@ -562,7 +569,7 @@
"source": [
"## Model visualization\n",
"\n",
"We will use the methon print.tree() to visualize our tree."
"We will use the method print.tree() to visualize our tree."
]
},
{
@ -598,12 +605,12 @@
"source": [
"## Testing the model\n",
"\n",
"We are using the definded method predict() to determine the classes of the Test dataset - those will be stored in the Y_pred which we will than compare to Y_test with the help of sklearn library function called accuracy_score"
"We are using definded method predict() to determine the classes of the Test dataset - those will be stored in the Y_pred which we will then compare to Y_test with the help of sklearn library function called accuracy_score"
]
},
{
"cell_type": "code",
"execution_count": 32,
"execution_count": 41,
"metadata": {},
"outputs": [
{
@ -612,7 +619,7 @@
"0.9333333333333333"
]
},
"execution_count": 32,
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
@ -639,6 +646,16 @@
"Our objective here is to predict if the customer will purchase the iPhone or not given their gender, age and salary."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### About the data \n",
"\n",
"Despite all the effort I couldn't find the origin of this data thus it shouldn't be used for any other purposes. \n",
"The dataset contains a set of 400 records under 4 attributes - Gender, Age, Salary and Class( whether the person made a purchase or not)."
]
},
{
"cell_type": "markdown",
"metadata": {},