Tag Archives: scikit-learn

How to add a dropout layer to a Deep Learning Model in Keras

    In deep learning, a dropout layer is a regularization technique that helps prevent overfitting by randomly dropping out (or turning off) a certain number of neurons during training. In other words, dropout layer randomly drops out a certain percentage of neurons in a layer, so that the neurons in the layer are not …

Learn By Example | How to add a dropout layer to a Deep Learning Model in Keras?

Learn By Example | How to add a dropout layer to a Deep Learning Model in Keras?     Deep learning models are complex algorithms that can be used to solve a variety of tasks, such as image recognition, natural language processing, and more. However, these models can sometimes “memorize” the training data too well, …

Learn By Example | How to setup a Deep Learning Model in Keras?

Learn By Example | How to setup a Deep Learning Model in Keras?   Deep learning is a branch of machine learning that uses neural networks to create models that can automatically learn from data. Keras is a popular open-source library for deep learning in Python, which provides a simple and user-friendly interface to create …

Data Science Coding | Keras and Tensorflow with Grid Search Cross Validation | IRIS Data | WACAMLDS

Keras and Tensorflow with Grid Search Cross Validation | IRIS Data Keras and TensorFlow are two powerful libraries that are used for building and training machine learning models. Keras is a high-level neural networks API, written in Python, that runs on top of TensorFlow. It is designed to make it easy to build and train …

Data Science Coding | SKLEARN XGBoost Classifier with Grid Search Cross Validation | WACAMLDS

SKLEARN XGBoost Classifier with Grid Search Cross Validation   XGBoost is a powerful and efficient implementation of the Gradient Boosting algorithm that is used to classify items into different categories. It is an ensemble method that combines the predictions of multiple weak models, such as decision trees, to make a final prediction. The technique uses …

SKLEARN Gradient Boosting Classifier with Monte Carlo Cross Validation

SKLEARN Gradient Boosting Classifier with Monte Carlo Cross Validation   Gradient Boosting Classifier is a machine learning technique used to classify items into different categories. It is an ensemble method that combines the predictions of multiple weak models, such as decision trees, to make a final prediction. The technique uses an iterative process where each …

SKLEARN Gradient Boosting Classifier with Grid Search Cross Validation

SKLEARN Gradient Boosting Classifier with Grid Search Cross Validation   Gradient Boosting Classifier is a machine learning technique used to classify items into different categories. It is an ensemble method that combines the predictions of multiple weak models, such as decision trees, to make a final prediction. The technique uses an iterative process where each …

IRIS Flower Classification using SKLEARN RandomForest Classifier with Monte Carlo Cross Validation

  In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: IRIS Flower Classification using SKLEARN RandomForest Classifier with Monte Carlo Cross Validation.   Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist …

IRIS Flower Classification using SKLEARN Random Forest Classifier with Grid Search Cross Validation

IRIS Flower Classification using SKLEARN Random Forest Classifier with Grid Search Cross Validation   The IRIS flower is a popular example in the field of machine learning. It is a type of flower that has different variations, such as the setosa, virginica, and versicolor. In this blog, we will be discussing how to classify the …

IRIS Flower Classification using SKLEARN DecisionTree Classifier with Monte Carlo Cross Validation

IRIS Flower Classification using SKLEARN DecisionTree Classifier with Monte Carlo Cross Validation   The IRIS flower is a popular example in the field of machine learning. It is a type of flower that has different variations, such as the setosa, virginica, and versicolor. In this blog, we will be discussing how to classify the IRIS …