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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
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Cross-Validation

Here you’ll find everything about cross-validation, whether it’s Python tutorials on implementing cross-validation with Python or conceptual articles describing different types.

Cross-validation is a technique for evaluating the performance of a machine-learning model. It is an essential step in the model development process. When we cross-validate a model, this involves splitting the training dataset into multiple subsets, called folds. We then train the model on one subset and evaluate it on the remaining subsets. This is done for each possible combination of training and evaluation subsets. The performance of the model is averaged across all of the splits. This allows us to train a model and evaluate it on different data. The result is a more accurate estimate of the model performance. Evaluating a model on multiple folds can provide a more accurate estimate of the model’s performance than using a single split of the data. This can be useful for choosing the best model or for fine-tuning model hyperparameters. It also helps to avoid overfitting.

predictive maintenance python machine learning tutorial iot manufacturing

Predictive Maintenance: Predicting Machine Failure using Sensor Data with XGBoost and Python

May 27, 2023January 8, 2023

Predictive maintenance is a game-changer for the modern industry. Still, it is based on a simple idea: By using machine … Read more

Using Random Search to Tune the Hyperparameters of a Random Decision Forest with Python

May 27, 2023April 7, 2022

Perfecting your machine learning model’s hyperparameters can often feel like hunting for a proverbial needle in a haystack. But with … Read more

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