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relataly.com

  • AI
    • Simple Regression
    • Classification: Two Class
    • Classification: Multi-Class
    • Clustering
    • Time Series Forecasting
    • Anomaly Detection
    • Natural Language
    • Recommender Systems
    • Reinforcement Learning
    • Responsible AI
  • Use Cases
    • Stock Market Forecasting
    • Algorithmic Trading
    • Sentiment Analysis
    • Churn Prediction
    • Fraud Detection
    • Predictive Maintenance
    • Marketing Automation
    • Customer Segmentation
    • Sales Forecasting
    • ChatBots
    • Fighting Crime
    • Risk Management
    • Image Recognition
  • Algorithms
    • CNNs
    • RNNs (LSTM)
    • Decision Trees
    • Random Decision Forests
    • Random Isolation Forest
    • Local Outlier Factor
    • Gradient Boosting
    • Collaborative Filtering
    • Content-based Filtering
    • K-Nearest Neighbors
    • K-Means
    • Affinity Propagation
    • Agglomerative Clustering
    • Logistic Regression
    • Naive Bayes
    • ARIMA
  • Data Science
    • Exploratory Data Analysis
    • Feature Engineering
    • Hyperparameter Tuning
    • Dimensionality Reduction
    • Model Interpretation
    • Data Visualization
    • Correlation
    • Measuring Performance
    • Cross-Validation
    • Vector Databases
    • SQLite
    • Data Science Environments
      • Anaconda
      • Azure Machine Learning
    • Python Libraries
      • Scikit-Learn
      • Tensorflow
      • Keras
      • Pytorch
      • PySpark
      • Chainer
      • OpenAI Gym
      • Seaborn
      • Fairlearn
      • Facebook Prophet
      • GeoPandas
  • Data & APIs
    • OpenAI API
    • REST APIs
    • NewsAPI
    • Coinmarketcap API
    • Coinbase API
    • Gate.io API
    • Yahoo Finance API
    • Statworx COVID-19 API
    • Twitter API
    • Reddit API
    • Kaggle Competitions
    • Synthetic Data
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Random Forest Classification

Random forest classification is a type of machine learning algorithm that is used for classification tasks. Classification is a type of supervised learning in which the goal is to predict a discrete class label (such as “spam” or “not spam”) for a given input. Random forest classification is an ensemble method, which means that it trains multiple decision trees on random subsets of the data and then averages their predictions to make a final prediction. This can provide a more robust and accurate model than a single decision tree, especially for complex and non-linear data. In random forest classification, the final prediction is made by taking the majority vote of the predicted class labels from all of the individual decision trees in the ensemble.

hyperparameter tuning titanic dataset machine learning

Tuning Model Hyperparameters with Grid Search at the Example of Training a Random Forest Classifier in Python

May 27, 2023July 6, 2020

Are you struggling to find the best hyperparameters for your machine learning model? With Python’s Scikit-learn library, you can use … Read more

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