Normalized Cross-Entropy In Machine Learning (Python Example)

Table of Contents What Is Normalized Cross-Entropy? What Is The Equation For Normalized Cross-Entropy? What Is The Difference Between Cross-Entropy And Normalized Cross-Entropy? How To Choose The Right Baseline For Calculating Normalized Cross-Entropy? How Does The Base Level Affect The Interpretation Of The Metric? Can Normalized Cross-entropy Be Used For Regression Problems? How To Calculate Normalized Cross-Entropy In Python How To Calculate Normalized Cross-Entropy For Validation Sets What Is Normalized Cross-Entropy?...

June 9, 2023 · 6 min · Mario Filho

Multiple Time Series Forecasting With N-BEATS In Python

Imagine having a robust forecasting solution capable of handling multiple time series data without relying on complex feature engineering. That’s where N-BEATS comes in! In this tutorial, I’ll break down its inner workings, walk you through the process of installing and configuring NeuralForecast to train an N-BEATS model in Python, and show you how to effectively prepare and split your time series data. Furthermore, we’ll explore hyperparameter tuning with Optuna....

June 2, 2023 · 15 min · Mario Filho

Multiple Time Series Forecasting With GRU In Python

So, you’ve already explored the world of LSTMs and now you’re curious about their sibling GRUs (Gated Recurrent Units) and how they can enhance your time series forecasting projects… Great! As machine learning practitioners, we’re always looking for ways to expand our knowledge and improve our model choices. In this tutorial, we’ll take a deep dive into GRUs, covering their inner workings, and comparing them to LSTMs. By the end of this tutorial, you’ll have a solid understanding of GRUs and be well-equipped to use them effectively in Python....

May 25, 2023 · 14 min · Mario Filho

Sales Forecasting For Multiple Products Using Python (Complete Guide)

As a data scientist, tackling sales forecasting for multiple products is a tough job. You know it’s essential for businesses, but dealing with different models, metrics, and complexities can be overwhelming. You might be feeling the pressure to deliver accurate forecasts to drive better decision-making and wondering how to tackle this challenge effectively. Don’t worry! I’m here to make this process easier and guide you through it. In this tutorial, I’ll simplify sales forecasting by walking you through these key steps:...

May 18, 2023 · 26 min · Mario Filho

Difference Between Demand Forecasting and Sales Forecasting

Demand forecasting and sales forecasting are two distinct but related concepts in business planning. Demand forecasting involves predicting the number of customers who will desire and potentially purchase a particular product or service in the future, helping businesses plan for production, inventory, and marketing. Sales forecasting, on the other hand, focuses on estimating the revenue a business will generate from selling its products or services, taking into account factors like pricing, competition, and marketing efforts....

May 5, 2023 · 7 min · Mario Filho

Bagging vs Boosting vs Stacking In Machine Learning

Bagging, boosting, and stacking are three ensemble learning techniques used to improve model performance. Bagging involves training multiple models independently on random subsets of data and then combining their predictions through a majority vote. Boosting focuses on correcting the errors made by previous weak models in a sequence to create a stronger model. Stacking combines multiple models by training a meta-model, which takes model predictions as input and outputs the final prediction....

May 2, 2023 · 23 min · Mario Filho

Ensemble Time Series Forecasting in Python Made Easy with AutoGluon

A fast, easy, and hands-off approach to creating ensemble models for time series forecasting is using AutoGluon. AutoGluon is an open-source AutoML library for deep learning. It’s a great tool for time series forecasting because it can automatically select the best models for your data and ensemble them together to create a more accurate model. It also has a built-in feature to handle missing values and can handle large datasets....

April 24, 2023 · 12 min · Mario Filho

CatBoost Hyperparameter Tuning Guide with Optuna

You’ve built a CatBoost model; now what? Hyperparameter tuning is the key to unlocking your model’s full potential. But if the thought of tackling this task feels daunting, you’re not alone. Once you’ve mastered the tips and tricks presented in this tutorial, you’ll be equipped with the skills to fine-tune any CatBoost model effectively. Let’s get started! Table of Contents Installing CatBoost and Optuna Which CatBoost Hyperparameters Should I Tune?...

April 19, 2023 · 7 min · Mario Filho

6 Free, High-Quality, Marketing Mix Modeling Datasets

Have you ever been excited to dive into understanding marketing mix modeling (MMM) for a project or just to learn, but got stuck trying to find quality datasets? You’re not alone! It can be a real pain trying to sift through endless data sources to find the perfect one for your needs. Whether you’re a student working on a project, a professional testing a new concept, or someone eager to master MMM techniques, I’ve got your back....

April 17, 2023 · 7 min · Mario Filho

5 Dynamic Time Warping (DTW) Libraries in Python With Examples

The world of time series analysis can be complex, and finding the right Python library for Dynamic Time Warping can be even more so. That’s where this tutorial comes in! My goal is to provide you with an easy-to-follow guide that will help you understand the various options available and make the right choice for your project. Whether you are a beginner or an expert, you will find valuable insights here....

April 13, 2023 · 6 min · Mario Filho