#randomforest

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The training algorithm for random forests applies the general technique of bootstrap aggregating, or bagging, to tree learners. #data #datascience #machinelearning #math #mathematics #statistics #analytics #dataanalytics #dataviz #learn #learning #study #studying #teach #knowledge #educator #education #illustrationoftheday #randomforest #ensemble

[shivam1605]

DECISION TREES AND RANDOM FOREST in Machine Learning

Decision Trees and Random Forest are among the most powerful and widely used algorithms in Machine Learning. In this video, we simplify the intuition and structure behind Decision Trees and explain how Random Forest combines multiple trees to deliver more accurate and stable predictions.

📊 If you want to understand how machines make decisions and why ensemble models outperform single algorithms, this video is for you!

📌 What You’ll Learn:

💡 Decision Trees Explained – Splits, nodes, and leaf structure made simple 🌲 Random Forest Demystified – How multiple trees work together for better results 📈 Overfitting & Pruning – Techniques to build smarter, more reliable models 🧠 Real-World Use Cases – Applications in customer churn, fraud detection, and more

🎓 Why Learn with Imarticus Learning?

✔️ Learn from experienced industry professionals ✔️ Flexible, structured learning programs ✔️ Mock tests, mentorship, and comprehensive study resources ✔️ Career-focused training that delivers real outcomes

🔥 Break into AI & ML — Build Smarter Models, Secure Top Jobs!

The Postgraduate Program in Data Science and Analytics (PGA) is a 6-month program designed for graduates and early professionals. It offers 100% job assurance, training across 10+ tools like Python, Power BI, and Tableau, 25+ hands-on projects, and 300+ learning hours. With salary hikes of up to 52% and a highest package of ₹22.5 LPA, this program prepares you for high-impact careers in data science and analytics.

Start building intelligent models—and a future-ready career—with Imarticus Learning.

[deepedataeducation]

Random Forest vs Decision Tree: The Ultimate Power Guide to Smarter Machine Learning Models

Decision Trees are simple yet powerful models for classification and prediction, but they can overfit easily. Random Forests overcome this by combining multiple trees to improve accuracy, reduce bias, and enhance generalization. Together, they form the foundation of smarter, more reliable machine learning systems Read More..

[shivam1605]

Random Forest Cloud Deployment for Beginners

Deploying ML on Cloud isn’t just about hosting your model—it’s about making it smarter, faster, and more reliable. One of the best ways to achieve this? Random Forest 🌲✨

In this Masterclass, discover how to implement a Random Forest Classifier to improve prediction accuracy for your sleep disorder detection project. Step by step, we’ll break down the logic, implementation, and evaluation of this powerful ensemble method.

📌 What You’ll Learn

1️⃣ Understanding Random Forest → Bagging, decision trees, and why Random Forest often outperforms single models.

2️⃣ Model Building in Python → Hands-on demo: train a Random Forest Classifier with Scikit-learn.

3️⃣ Performance Tuning → Fine-tune hyperparameters like n_estimators and max_depth for maximum accuracy.

4️⃣ Evaluation Metrics → Random Forest vs. Decision Tree: accuracy, confusion matrix, and F1-score.

🌐 Go Beyond Spreadsheets – Explore the Cloud Frontier of Data Analytics!

🎓 Choose your path:

🔹 Postgraduate Program in Data Science and Analytics (PGA) – 6 months | For graduates & early professionals | 100% job assurance via 2,000+ hiring partners | 25+ projects | 10+ tools (Python, Power BI, Tableau) | Avg. 52% salary hike | Highest package: ₹22.5 LPA

🔹 Executive Post Graduate Program in Data Science & AI (EPGP-DSAI) – 11 months | For working professionals | Career support (resume building, mock interviews, job portal) | 35+ projects | 10,000+ successful career transitions | Avg. 52% salary hike

📢 Ready to make your ML career cloud-ready? 👉 Learn more with Imarticus Learning.

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Running a Random Forest

from pandas import Series, DataFrame import pandas as pd import numpy as np import os import matplotlib.pylab as plt from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report import sklearn.metrics # Feature Importance from sklearn import datasets from sklearn.ensemble import ExtraTreesClassifier

Load the dataset

data = pd.read_csv("C:\Users\guy3404\OneDrive - MDLZ\Documents\Cross Functional Learning\AI COP\Coursera\machine_learning_data_analysis\Datasets\tree_addhealth.csv")

data.head()

Getting information aboubt the dataset

data.info()

Total size of data

len(data)

We observe some of the columns of the dataset contains null values . We need to drop them

Drop null values from dataset

data_clean = data.dropna()

data_clean.dtypes

data_clean.describe()

Length of dataset after dropping null values

len(data_clean)

Split into training and testing sets

predictors = data_clean[['BIO_SEX','HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN','age', 'ALCEVR1','ALCPROBS1','marever1','cocever1','inhever1','cigavail','DEP1','ESTEEM1','VIOL1', 'PASSIST','DEVIANT1','SCHCONN1','GPA1','EXPEL1','FAMCONCT','PARACTV','PARPRES']]

targets = data_clean.TREG1

pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, targets, test_size=.4)

pred_train.shape pred_test.shape tar_train.shape tar_test.shape

Build model on training data

from sklearn.ensemble import RandomForestClassifier

classifier=RandomForestClassifier(n_estimators=25) classifier=classifier.fit(pred_train,tar_train)

predict using random forest classifier on test data

predictions=classifier.predict(pred_test)

Print confusion matrix and accuracy score

sklearn.metrics.confusion_matrix(tar_test,predictions)

sklearn.metrics.accuracy_score(tar_test, predictions)

fit an Extra Trees model to the data

model = ExtraTreesClassifier() model.fit(pred_train,tar_train)

Get feature importances

feature_importances = model.feature_importances_

Create a Series with feature importances and corresponding feature names

feature_importance_series = pd.Series(feature_importances, index=pred_train.columns)

Sort features based on importance

sorted_feature_importance = feature_importance_series.sort_values(ascending=False)

Plot the feature importances

plt.figure(figsize=(10, 6)) sorted_feature_importance.plot(kind='barh') plt.title('Feature Importance') plt.xlabel('Importance Score') plt.show()

#Running different number of trees and see the effect of that on the accuracy of the prediction

trees=range(25) accuracy=np.zeros(25)

for idx in range(len(trees)): classifier=RandomForestClassifier(n_estimators=idx + 1) classifier=classifier.fit(pred_train,tar_train) predictions=classifier.predict(pred_test) accuracy[idx]=sklearn.metrics.accuracy_score(tar_test, predictions)

plt.cla() plt.plot(trees, accuracy)

Random forest analysis was performed to evaluate the importance of series of variables in predicting whether a person is a regular smoker or not. We observed that out of all features, marijuana use has the highest feature importance, followed by deviance and GPA. The random model could predict with an accuracy score of 85%.

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