(testing signal)

Tag: overfitting

An Introduction to Bias-Variance Tradeoff

I recently discussed model underfitting and overfitting. Essentially, these two concepts describe different ways that the model can fail to match your data set. Underfitting refers to making a model that’s not complex enough to accurately represent your data and misses trends in the data set. Overfitting refers to a situation where the model is too complex for the data set, and indicates trends in the data set that aren’t actually there.

Another way we can think about these topics is through the terms bias and variance. These two terms are fundamental concepts in data science and…

Overfitting and Conceptual Soundness

How feature usage informs our understanding of overfitting in deep networksPhoto by Shane Aldendorff on UnsplashOverfitting is a central problem in machine learning that is strongly tied to the reliability of a learned model when it is deployed on unseen data. Overfitting is often measured — or even defined — by the difference in accuracy obtained by a model on its training data, compared to on previously unseen validation data. While this is a useful metric that broadly…

Exploiting Google Images To Search For a Data Science Content

Some tips on how to improve your search strategies on GoogleImage by Author

formulate your problemtranslate it into a questionbrowse the first level of resultsfocus on the answer to your problem

Predicting Wine Prices with Tuned Gradient Boosted Trees

Using Optuna to find the optimal hyperparameter combination

Many popular machine learning libraries use the concept of hyperparameters. These can be though of as configuration settings or controls for your machine learning model. While many parameters are learned or solved for during the fitting of your model (think regression coefficients), some inputs require a data scientist to specify values up front. These are the hyperparameters which are then used to build and train the model.

One example in gradient boosted decision trees is the depth of a decision tree. Higher values yield potentially more complex trees that can pick up on certain relationships, while smaller trees may be able to generalize better and avoid overfitting to our outcome — potentially leading to issues when predicting unseen data.


New Study uses Federated Learning to Predict Covid-19 Outcomes

  • New study used Federated Learning to predict severity of Covid-19 for E.R. patients.
  • Significant improvements seen in central vs. local models.
  • Model slated for use in production in the near future.

Many ethical and legal challenges surround COVID-19 data analysis, including data ownership, data security, and privacy issues. As a result, healthcare providers have typically preferred models validated on their own data. However, this limits the scope of analysis that can be performed, often resulting in AI models that lack diversity, suffer from overfitting, and demonstrate poor generalization. One recent study titled Federated learning for predicting clinical outcomes in patients with COVID-19, published in September 15 issue of Nature Medicine [1], offered a solution to these problems: Federated Learning (FL).


Equivalence of Regularised Least Squares and Maximising the Posterior Probability Function

Machine Learning Derivation

Why regularized least squares is equivalent to the maximum posterior solution and is the optimal algorithm for normally distributed data

Photo by ThisisEngineering RAEng on Unsplash

This article compares Tikonov L2 regularisation to the maximum posterior probability function. This one is quite algebra dense so I recommend picking up a pen and paper and trying it yourself!

One of the problems with many machine learning models are their tendency to overfit data. Overfitting is a phenomenon in machine learning that happens when the model learns the training data too well, making its ability to generalize worse.

There is a delicate balance between the bias and the variance of a learning machine.


Understanding Regularisation

Regularisation is a powerful tool that can help tune models

Photo by Lucas Benjamin on Unsplash

At the start of this year, I had a very limited understanding of regularisation. To me, it was simply a tool to use when overfitting. If the model was having higher validation loss than train loss, adding L1 or L2 regularisation might be able to solve this issue. The truth is, there are many different ways to look at regularization and it is a powerful tool that can help tune models that are behaving differently.

Before understanding the solution (regularisation) we must first understand the problem which is overfitting.

Let us start from the overfitting diagnosis that all of us are familiar with: when the validation/test loss is higher than the training loss.


These 9 Insights From Helping UK Government Handle COVID-19 Will Change Your Mind About Data…

Is it statistics or ML? Wait, isn’t ML just advanced statistics? I have come across several versions of these questions in my 14 years career working with data. There are debates between high-profile experts, articles, and even peer-reviewed articles in prestigious journals on this topic. It’s crazy.

Honestly, this is a useless, (seemingly) inconclusive debate. ML is by definition concerned with learning from data. A key component of learning from data often requires transforming raw data into summary variables. A good chunk of statistics is all about summarising data. We now have an increasingly vast amount of data and require ingenious algorithmic approaches. A lot of these have been developed by the community sitting in computer science departments.


Build Better Regression Models With LASSO


A Variational Information Bottleneck (VIB) Based Method to Compress Sequential Networks for Human…

Compress LSTMs and infer models on the edge


A guide to XGBoost hyperparameters

What is the one machine learning algorithm — if you ask — that consistently gives superior performance in regression and classification?

XGBoost it is. It is arguably the most powerful algorithm and is increasingly being used in all industries and in all problem domains —from customer analytics and sales prediction to fraud detection and credit approval and more.

It is also a winning algorithm in many machine learning competitions. In fact, XGBoost was used in 17 out of 29 data science competitions on the Kaggle platform.

Not just in businesses and competitions, XGBoost has been used in scientific experiments such as the Large Hadron Collider (the Higgs Boson machine learning challenge).

A key to its performance is its hyperparameters.


Data Augmentation Compilation with Python and OpenCV

Prevent Your Model from Overfitting

Photo by on

Data augmentation is a technique to increase the diversity of dataset without an effort to collect any more real data but still help improve your model accuracy and prevent the model from overfitting. In this post, you will learn to implement the most popular and efficient data augmentation procedures for object detection task using Python and OpenCV.

The set of data augmentation methods that are about to be introduced includes:

  1. Random Crop
  2. Cutout
  3. ColorJitter
  4. Adding Noise
  5. Filtering

Firstly, let’s import several libraries and prepare some necessary subroutines before going ahead.

The below image is used as a sample image during this post.

Image: tr03–14–18–1-FRONT.jpg

Machine learning with H2O in R / Python

In this blog, we shall discuss about how to use H2O to build a few supervised machine learning models. H2O is a Java-based software for data modeling and general computing, with the primary purpose of it being a distributed, parallel, in memory processing engine. It needs to be installed first (instructions) and by default an H2O instance will run on localhost:54321. Additionally, one needs to install R/python clients to to communicate with the H2O instance. Every new R / python session first needs to initialize a connection between the python client and the H2O cluster.

The problems to be described in this blog appeared in the exercises / projects in the Coursera course “Practical Machine Learning on H2O,” by H2O.


Accelerate your Hyperparameter optimization with scikit-optimize

Get the right balance between underfitting and overfitting by finding optimal hyperparameters for any model

photo by Author

Despite being one of the last stages of creating a model, Hyperparameter optimization (“HPO”) can make all the difference between a good model, which generalizes well, or ugly overfitting, which performs great with the training data but is much worse on the validation set.

This is especially the case with popular Tree Base models such as Random Forest, XGBoost, or CatBoost. Usually, the base model will badly overfit your data. On the other hand, trying to manually increase the bias by setting some hyperparameters like “max_depth” or “max_features” in RandomForest often causes significant underfitting.


A Quick Guide to Decision Trees


Bias vs Variance, Overfitting vs Underfitting

This is related to confusing signal with noise.

– Bias: distance of the results to the target.
– Variance: the spread of the results

– Overfitting: The model get more complex and fits too much to the noise from the data. This results in low error on training set, but high error on new data, test/validation sets.
– Underfitting: Model too simple does not capture the underlying trend of the data and does not fit the data well enough. Low variance but high bias.