Python-for-Machine-Learning/C3/Decision-Tree/English - Revision history

Madhurig at 06:29, 4 July 2025

2025-07-04T06:29:05Z

Madhurig at 08:01, 28 June 2025

2025-06-28T08:01:18Z

Madhurig at 07:45, 28 June 2025

2025-06-28T07:45:19Z

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Nirmala Venkat: Created page with "
{| border="1" |- || '''Visual Cue''' || '''Narration''' |- |- style="border:0.5pt solid #000000;padding-top:0cm;pad..."

2025-06-06T12:49:31Z

Created page with " <div style="margin-left:1.27cm;margin-right:0cm;"></div> {| border="1" |- || '''Visual Cue''' || '''Narration''' |- |- style="border:0.5pt solid #000000;padding-top:0cm;pad..."

New page

<div style="margin-left:1.27cm;margin-right:0cm;"></div>
{| border="1"
|-
|| '''Visual Cue'''
|| '''Narration'''
|-
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| <div style="color:#000000;">Show slide:</div>

<div style="color:#000000;">'''Welcome'''</div>

|| <span style="color:#000000;">Welcome to the Spoken Tutorial on</span><span style="color:#000000;">''' Decision Tree'''</span><span style="color:#000000;">.</span>
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| <div style="color:#000000;">Show Slide:</div>

<div style="color:#000000;">'''Learning Objectives'''</div>

|| In this tutorial, we will learn about
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Decision Tree'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Decision Tree Structure and Nodes'''</div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| <div style="color:#000000;">Show Slide:</div>

<div style="color:#000000;">'''System Requirements'''</div>
<div style="color:#000000;"></div>
|| To Record this tutorial, I am using
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#000000;">'''Ubuntu Linux </span>OS<span style="color:#000000;"> 2</span>4<span style="color:#000000;">.04'''</span></div>
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#000000;">'''Jupyter Notebook'''</span><span style="color:#000000;"> </span><span style="color:#000000;">'''IDE'''</span></div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| <div style="color:#000000;">Show Slide:</div>

<div style="color:#000000;">'''Prerequisite'''</div>

To follow this tutorial,<div style="margin-left:0.635cm;margin-right:0cm;"></div>
|| To follow this tutorial,
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#000000;">The learner must have basic </span>knowledge of<span style="color:#000000;"> </span>'''P<span style="color:#000000;">ython</span>.'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#000000;">For prerequisite </span><span style="color:#000000;">'''Python'''</span><span style="color:#000000;"> tutorials, please visit this website.</span></div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| <div style="color:#000000;">Show Slide:</div>

<div style="color:#000000;">'''Code files'''</div>
||
* <div style="margin-left:1.27cm;margin-right:0cm;">The files used in this tutorial are provided in the '''Code files '''link.</div>
* <div style="color:#252525;margin-left:1.27cm;margin-right:0cm;">Please download and extract the files.</div>
* <div style="color:#252525;margin-left:1.27cm;margin-right:0cm;">Make a copy and then use them while practicing.</div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Show Slide:

'''Decision Tree'''
||
* <div style="margin-left:1.27cm;margin-right:0cm;">A '''decision tree''' is a tool used in machine learning that helps make decisions.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">It uses a tree like structure to make predictions.</div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Show Slide:

'''Working of Decision Tree'''

'''Show dt.png img'''
||
* <div style="margin-left:1.27cm;margin-right:0cm;">The '''root node''' starts the decision tree with a question or condition.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Based on the answer, we follow a '''branch''' to another '''node.'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">A '''branch''' connects nodes, where each '''node '''represents a condition with outcomes.</div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| '''Show dt.png img'''

<div style="color:#ff0000;"></div>
||
* <div style="margin-left:1.27cm;margin-right:0cm;">This new '''node''' poses another question or condition.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">We repeat this process of asking questions and following '''branches.'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Finally, we reach a '''leaf node''', which gives us the final decision or outcome.</div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Hover over the files
|| I have created required files for the demonstration of Decision Tree.<span style="color:#ff0000;"> </span>
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Open the file drug200.csv and point to the fields as per narration.
|| To implement the '''Decision Tree model, '''we use the '''drug200 dot csv '''dataset.

Here, we analyze patient’s data to predict the most suitable drug for them.

'''drug200 '''dataset contains '''Age, Sex, BP, Cholesterol, Na to K ratio''' and '''Drug.'''

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Point to the '''DecisionTree.pynb'''
|| '''DecisionTree dot ipynb''' is the ipython notebook file for this demonstration.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Press '''Ctrl,Alt and T''' keys

Type '''conda activate ml'''

Press '''Enter'''
|| Let us open the Linux terminal by pressing '''Ctrl, Alt''' and '''T '''keys together.
Activate the machine learning environment by typing
'''conda space activate''' '''space ml'''
Press '''Enter.'''
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Go to the '''Downloads '''folder

Type '''cd Downloads'''
Press '''Enter'''
Type '''jupyter notebook'''

Press '''Enter'''
|| I have saved my code file in the '''Downloads '''folder.

Please navigate to the respective folder of your code file location.

Type, '''jupyter space notebook '''and press Enter to open Jupyter Notebook.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Show '''Jupyter Notebook Home page''':

Click on '''DecisionTree.ipynb''' file
|| We can see the '''Jupyter Notebook''' '''Home page''' has opened in the web browser.

Click the''' DecisionTree dot ipynb '''file to open it.

<div style="color:#000000;">Note that each cell will have the output displayed in this file.</div>
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| <div style="color:#000000;">Highlight The lines:</div>
<div style="color:#000000;">'''import numpy as np'''</div>
<div style="color:#000000;">'''import pandas as pd'''</div>
<div style="color:#000000;">'''from sklearn.model_selection import train_test_split '''</div>
<span style="color:#000000;">'''from sklearn.tree import DecisionTreeClassifier '''</span>
Press '''Shift+Enter'''

|| These are the necessary libraries to be imported for the Decision Tree.

Please remember to Execute each cell by pressing '''Shift and Enter''' to get output.

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight the lines:
'''df_drug=pd.read_csv("drug200.csv") '''
'''df_drug.head()'''
Press '''Shift+Enter'''
|| We start by loading the '''dataset''' from a CSV file and display the first few rows.

<div style="color:#000000;"></div>
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight The lines:
'''print("Length of Dataset:", len(df_drug)) print("Dataset Shape:", df_drug.shape)'''
Press '''Shift+Enter'''
|| Then we print the '''number of rows''' and the '''shape''' of the dataset.

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight the lines:
'''le_sex = LabelEncoder() '''
'''le_BP = LabelEncoder() '''
Press '''Shift+Enter'''
|| Next, we encode the categorical variables like '''Sex and BP''' into numerical values.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight The lines:

'''x=df_drug.drop(columns=['Drug']).values'''
'''y = df_drug['Drug'].values'''
Press '''Shift+Enter'''
|| We then separate the '''features x''' and the target '''variable y'''.

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight the lines:

'''x'''
|| Now we print the values of '''features.'''
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight the lines:

'''y'''
|| Similarly, we print the values of the target.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight the lines:

'''print("\nNumber of Duplicate Rows:", df_drug.duplicated().sum())'''
'''df = df_drug.drop_duplicates()'''
'''print("Dataset Shape After Removing Duplicates:", df.shape)'''
|| Next, we check for '''duplicate rows''' and remove them if found.

We also print the '''dataset shape''' after removing the duplicates.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight the lines:
'''numerical_columns = df.select_dtypes(include=['int64', 'float64']).columns'''
'''scaler = StandardScaler()'''
|| Now we use '''StandardScaler''' to standardize the numerical columns.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight the lines:

'''df.hist(figsize=(10, 8), bins=20, color='skyblue', edgecolor='black')'''

'''plt.suptitle("Distribution of Numerical Features")'''

'''plt.show()'''
|| We then visualize the '''data distribution''' for numerical columns.

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight the lines:

'''x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=100)'''

Press '''Shift+Enter'''
|| Now, we split the data into '''training''' and '''testing sets.'''
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight the lines:

'''clf_entropy = DecisionTreeClassifier(criterion="entropy", random_state=5, max_depth=4, min_samples_leaf=5)'''

Press '''Shift+Enter'''
|| After that we create a '''decision tree classifier''' using '''entropy''' as the criterion.
'''Entropy''' is the measure of disorder in the dataset.
It helps to classify the features into '''root '''and '''branches''' of the '''decision tree.'''

By navigating through the root and branches, we arrive at a decision of classes.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight the lines:

'''clf_entropy.fit(x_train, y_train)'''
'''y_pred_train = clf_entropy.predict(x_train) '''
|| We then fit the classifier to the '''training data.'''

Once trained, we predict the '''target values''' for the training set.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight the lines

'''print("Training Accuracy is", accuracy_score(y_train, y_pred_train) * 100 )'''
|| Now we print the '''training accuracy.'''
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight the lines:'''y_pred_en = clf_entropy.predict(x_test) '''

'''y_pred_en '''
|| Next we make predictions on the test data.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight The lines:

'''accuracy = round(accuracy_score(y_test, y_pred_en) * 100, 3)'''

'''print("Accuracy is", accuracy)'''

Press '''Shift+Enter'''
|| Then we print the''' test accuracy'''
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight the output
|| The accuracy is '''98.33''' which indicates the model performs very well.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight the lines:

'''cm = confusion_matrix(y_test, y_pred_en) '''
'''plt.figure(figsize=(10, 7)) '''
Press '''Shift+Enter'''
|| To analyze model performance further, we create and display a '''confusion matrix'''.

It shows how well the model is correctly classifying the instances.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight the lines:

'''report = classification_report(y_test, y_pred_en,zero_division=0) '''

'''print("Classification Report:") '''

Press '''Shift+Enter'''
|| We also generate and print the''' classification report.'''

This report gives '''precision, recall, f1-score''', and '''support''' for each class.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight the lines:

'''classes = np.unique(y)'''
'''y_test_bin = label_binarize(y_test, classes=classes)'''
'''y_score = clf_entropy.predict_proba(x_test)'''

|| Now we get all unique target classes from the dataset.
Next, we '''binarize''' y underscore test for multi class ROC plotting.

We binarize to handle multi class ROC as it needs binary format.

Then, we predict class probabilities using the model.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight the lines:

'''fpr = dict()'''
'''tpr = dict()'''
'''roc_auc = dict()'''
'''n_classes = len(classes)'''
'''for i in range(n_classes):'''
'''plt.legend(loc="lower right")'''
'''plt.grid(True)'''
'''plt.show()'''

|| We plot the '''ROC curve''' for each class using the predicted probabilities.

The '''ROC curve''' shows how well the model distinguishes between classes.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Show the output:

|| The output displays the '''multi class ROC curve''' of our classifier.

'''DrugA, DrugB, DrugC''' have an '''AUC score''' of '''1''', indicating perfect classification.

'''DrugX''' and''' DrugY '''have''' AUC scores '''of '''0.96 '''and''' 0.98''', which are very high.

The closer the curve is to the top left, the better the model performs.

Here, all curves are close to the top left corner of the plot.

So, our classifier performs very well for all classes.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Highlight the lines:

'''feature_names = df_drug.columns[:-1] '''

'''plt.figure(figsize=(29, 10)) '''
|| Then we extract the column names excluding '''Drug '''for tree '''visualization.'''

Next, we set the figure size and plot the '''decision tree'''.

Then we save and display the''' tree visualization''' as a PNG image file.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Show the output
|| The tree helps classify which drug to give based on patient features.

It first checks the Na to K value, then splits further using BP, Age, and Cholesterol.

Each colored box shows sample count and predicted drug type.

The tree splits until leaves mostly contain one drug, showing zero entropy.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Narration
|| Thus, we built a decision tree to predict '''drug''' types based on patient data.

The model showed '''high accuracy''', indicating '''strong predictive performance.'''
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Show slide:

'''Summary'''
|| This brings us to the end of the tutorial. Let us summarize.

In this tutorial, we have learnt about
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Decision Tree'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Decision Tree Structure and Nodes'''</div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Show Slide:

'''Assignment'''

As an assignment, please do the following
|| As an assignment, please do the following:

<span style="background-color:#ffffff;">Replace the max underscore depth as shown here.</span>

<span style="background-color:#ffffff;">Observe the change in Testing accuracy</span>.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Show Slide:

'''Assignment Solution'''

'''Show x img'''
|| After completing the assignment, the output should match as the expected result.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Show Slide:

'''FOSSEE Forum'''
|| For any general or technical questions on '''Python for Machine Learning''', visit the '''FOSSEE forum''' and post your question
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Show Slide:

'''Thank you'''

|| This is '''Harini Theiveegan''',<span style="color:#000000;"> a FOSSEE Summer Fellowship 2025, IIT Bombay signing off</span>

<div style="color:#000000;">Thanks for joining</div>
|-
|}

@@ Line 36: / Line 36: @@
 To follow this tutorial,
 || To follow this tutorial,
-* The learner must have basic knowledge of '''Python.'''
+* The learner must have basic knowledge of '''Python'''.
 * For prerequisite '''Python''' tutorials, please visit this website.
@@ Line 64: / Line 64: @@
 ||
 * The '''root node''' starts the decision tree with a question or condition.
-* Based on the answer, we follow a '''branch''' to another '''node.'''
+* Based on the answer, we follow a '''branch''' to another '''node'''.
-* A '''branch''' connects nodes, where each '''node '''represents a condition with outcomes.
+* A '''branch''' connects nodes, where each '''node ''' represents a condition with outcomes.
 |-
 || '''Show dt.png img'''
 ||
 * This new '''node''' poses another question or condition.
@@ Line 78: / Line 76: @@
 |-
 || Hover over the files
-|| I have created required files for the demonstration of Decision Tree.<span style="color:#ff0000;">
+|| I have created required files for the demonstration of Decision Tree.
 |-
 || Open the file drug200.csv and point to the fields as per narration.
-|| To implement the '''Decision Tree model, '''we use the '''drug200 dot csv '''dataset.
+|| To implement the '''Decision Tree model, we use the '''drug200 dot csv '''dataset.
 Here, we analyze patient’s data to predict the most suitable drug for them.
@@ Line 89: / Line 87: @@
 |-
 || Point to the '''DecisionTree.pynb'''
-|| '''DecisionTree dot ipynb''' is the ipython notebook file for this demonstration.
+|| '''DecisionTree dot ipynb''' is the '''ipython notebook''' file for this demonstration.
 |-
 || Press '''Ctrl,Alt and T''' keys
@@ Line 97: / Line 95: @@
 Press '''Enter'''
 || Let us open the Linux terminal by pressing '''Ctrl, Alt''' and '''T '''keys together.
 Activate the machine learning environment by typing
 '''conda space activate''' '''space ml'''
 Press '''Enter.'''
 |-
@@ Line 112: / Line 112: @@
 Please navigate to the respective folder of your code file location.
-Type, '''jupyter space notebook '''and press Enter to open Jupyter Notebook.
+Type, '''jupyter space notebook ''' and press '''Enter''' to open '''Jupyter Notebook'''.
 |-
 || Show '''Jupyter Notebook Home page''':
@@ Line 130: / Line 130: @@
 Press '''Shift+Enter'''
-|| These are the necessary libraries to be imported for the Decision Tree.
+|| These are the necessary libraries to be imported for the '''Decision Tree'''.
 Please remember to Execute each cell by pressing '''Shift and Enter''' to get output.
@@ Line 139: / Line 139: @@
 '''df_drug.head()'''
 Press '''Shift+Enter'''
-|| We start by loading the '''dataset''' from a CSV file and display the first few rows.
+|| We start by loading the '''dataset''' from a '''CSV''' file and display the first few rows.
@@ Line 145: / Line 145: @@
 || Highlight The lines:
 '''print("Length of Dataset:", len(df_drug)) print("Dataset Shape:", df_drug.shape)'''
-Press '''Shift+Enter'''
+Press '''Shift+Enter'''.
 || Then we print the '''number of rows''' and the '''shape''' of the dataset.
@@ Line 153: / Line 154: @@
 '''le_BP = LabelEncoder() '''
 Press '''Shift+Enter'''
 || Next, we encode the categorical variables like '''Sex and BP''' into numerical values.
 |-
 || Highlight The lines:
@@ Line 177: / Line 179: @@
 '''print("\nNumber of Duplicate Rows:", df_drug.duplicated().sum())'''
 '''df = df_drug.drop_duplicates()'''
-'''print("Dataset Shape After Removing Duplicates:", df.shape)'''
+'''print("Dataset Shape After Removing Duplicates:", df.shape)'''.
-|| Next, we check for '''duplicate rows''' and remove them if found.
 We also print the '''dataset shape''' after removing the duplicates.
@@ Line 208: / Line 211: @@
 '''clf_entropy = DecisionTreeClassifier(criterion="entropy", random_state=5, max_depth=4, min_samples_leaf=5)'''
-Press '''Shift+Enter'''
+Press '''Shift+Enter'''.
 || After that we create a '''decision tree classifier''' using '''entropy''' as the criterion.
 '''Entropy''' is the measure of disorder in the dataset.
 It helps to classify the features into '''root '''and '''branches''' of the '''decision tree.'''
@@ Line 225: / Line 231: @@
 || Highlight the lines
-'''print("Training Accuracy is", accuracy_score(y_train, y_pred_train) * 100 )'''
+'''print("Training Accuracy is", accuracy_score(y_train, y_pred_train) * 100 )'''.
 || Now we print the '''training accuracy.'''
 |-
@@ Line 263: / Line 270: @@
 || We also generate and print the''' classification report.'''
-This report gives '''precision, recall, f1-score''', and '''support''' for each class.
+This report gives '''precision''', '''recall''', '''f1-score''', and '''support''' for each class.
 |-
 || Highlight the lines:
@@ Line 294: / Line 301: @@
 |-
 || Show the output:
 || The output displays the '''multi class ROC curve''' of our classifier.

@@ Line 1: / Line 1: @@
 {| border="1"
@@ Line 300: / Line 297: @@
 || The output displays the '''multi class ROC curve''' of our classifier.
-'''DrugA, DrugB, DrugC''' have an '''AUC score''' of '''1''', indicating perfect classification.
+Let us see about the AUC score that is area under the curve.
 '''DrugX''' and''' DrugY '''have''' AUC scores '''of '''0.96 '''and''' 0.98''', which are very high.
@@ Line 315: / Line 314: @@
 '''plt.figure(figsize=(29, 10)) '''
-|| Then we extract the column names excluding '''Drug '''for tree '''visualization.'''
+|| Then we extract the column names excluding '''Drug ''' for tree '''visualization.'''
 Next, we set the figure size and plot the '''decision tree'''.
@@ Line 338: / Line 337: @@
 '''Summary'''
-|| This brings us to the end of the tutorial. Let us summarize.
 In this tutorial, we have learnt about
 * '''Decision Tree'''
 * '''Decision Tree Structure and Nodes'''
 |-

Python-for-Machine-Learning/C3/Decision-Tree/English - Revision history

Madhurig at 06:29, 4 July 2025

Madhurig at 08:01, 28 June 2025

Madhurig at 07:45, 28 June 2025

Nirmala Venkat: Created page with " {| border="1" |- || '''Visual Cue''' || '''Narration''' |- |- style="border:0.5pt solid #000000;padding-top:0cm;pad..."

Nirmala Venkat: Created page with "
{| border="1" |- || '''Visual Cue''' || '''Narration''' |- |- style="border:0.5pt solid #000000;padding-top:0cm;pad..."