AI News, Machine Learning FAQ

Machine Learning FAQ

Index In short, the general strategies are to For the first point, it may help to plot learning curves, plotting the training vs.

In my experience, ensembling is probably the most convenient way to build robust predictive models on somewhat small-sized datasets.

Regarding the third point, I usually start a predictive modeling task with the simplest model as a benchmark: usually logistic regression.

How can I avoid overfitting?

Ankit Awasthi , co-CEO of a global high-frequency-trading firm, and I have talked about the steps to remove overfitting in data-science trading.

TL;DR: (1) Do future-testing and not back-testing (2) Walk-forward measurement In this video ( Data Science + FinTech meetup group in New York City area ), what we have faced in this area and the rules of thumb we have learned.

Most people who ask me about how to detect good quants from bad quants, I talk about one thing Make sure the portfolio manager is not over-fitting.

An old-school approach to finding good trading strategies was to take a bunch of data and find the model and paramset that would have had the highest profits on that data.

Let's look at a few improvements quantitative portfolio managers employed in an effort to reduce over-fitting to past data and to improve trading profits in future unseen data.

For instance, if the market has become very volatile and we see that our paramset is not well adjusted to this new regime, the portfolio manager would want to recalibrate the paramset to this new regime, since it typically continues for a while.

The right way to measure expected profits would require us to see this strategy updation process as an essential part of the pipeline and to ensure that we never measure expected profits on a trading day with a strategy that would have been constructed looking at data not available till the day before.

Overfitting in Machine Learning: What It Is and How to Prevent It

Did you know that there’s one mistake… …that thousands of data science beginners unknowingly commit?

But don’t worry: In this guide, we’ll walk you through exactly what overfitting means, how to spot it in your models, and what to do if your model is overfit.

Next, we try the model out on the original dataset, and it predicts outcomes with 99% accuracy… wow!

When we run the model on a new (“unseen”) dataset of resumes, we only get 50% accuracy… uh-oh!

In predictive modeling, you can think of the “signal” as the true underlying pattern that you wish to learn from the data.

If you sample a large portion of the population, you’d find a pretty clear relationship: This is the signal.

it has too many input features or it’s not properly regularized), it can end up “memorizing the noise” instead of finding the signal.

In statistics, goodness of fit refers to how closely a model’s predicted values match the observed (true) values.

model that has learned the noise instead of the signal is considered “overfit” because it fits the training dataset but has poor fit with new datasets.

Underfitting occurs when a model is too simple – informed by too few features or regularized too much – which makes it inflexible in learning from the dataset.

too complex (high variance) is a key concept in statistics and machine learning, and one that affects all supervised learning algorithms.

key challenge with overfitting, and with machine learning in general, is that we can’t know how well our model will perform on new data until we actually test it.

For example, it would be a big red flag if our model saw 99% accuracy on the training set but only 55% accuracy on the test set.

Then, we iteratively train the algorithm on k-1 folds while using the remaining fold as the test set (called the “holdout fold”).

It won’t work everytime, but training with more data can help algorithms detect the signal better.

This is like the data scientist's spin on software engineer’s rubber duck debugging technique, where they debug their code by explaining it, line-by-line, to a rubber duck.

For example, you could prune a decision tree, use dropout on a neural network, or add a penalty parameter to the cost function in regression.

Bagging uses complex base models and tries to 'smooth out' their predictions, while boosting uses simple base models and tries to 'boost' their aggregate complexity.

While these concepts may feel overwhelming at first, they will ‘click into place’ once you start seeing them in the context of real-world code and problems.


In short, the general strategies are to For the first point, it may help to plot learning curves, plotting the training vs.

In my experience, ensembling is probably the most convenient way to build robust predictive models on somewhat small-sized datasets.

Regarding the third point, I usually start a predictive modeling task with the simplest model as a benchmark: usually logistic regression.

Train/Test Split and Cross Validation in Python

I’ll explain what that is — when we’re using a statistical model (like linear regression, for example), we usually fit the model on a training set in order to make predications on a data that wasn’t trained (general data).

As mentioned, in statistics and machine learning we usually split our data into to subsets: training data and testing data (and sometimes to three: train, validate and test), and fit our model on the train data, in order to make predictions on the test data.

We don’t want any of these things to happen, because they affect the predictability of our model — we might be using a model that has lower accuracy and/or is ungeneralized (meaning you can’t generalize your predictions on other data).

Let’s see what under and overfitting actually mean: Overfitting means that model we trained has trained “too well” and is now, well, fit too closely to the training dataset.

Basically, when this happens, the model learns or describes the “noise” in the training data instead of the actual relationships between variables in the data.

In contrast to overfitting, when a model is underfitted, it means that the model does not fit the training data and therefore misses the trends in the data.

The training set contains a known output and the model learns on this data in order to be generalized to other data later on.

Let’s load in the diabetes dataset, turn it into a data frame and define the columns’ names: Now we can use the train_test_split function in order to make the split.

Now we’ll fit the model on the training data: As you can see, we’re fitting the model on the training data and trying to predict the test data.

Here is a summary of what I did: I’ve loaded in the data, split it into a training and testing sets, fitted a regression model to the training data, made predictions based on this data and tested the predictions on the test data.

What if one subset of our data has only people from a certain state, employees with a certain income level but not other income levels, only women or only people at a certain age?

Here is a very simple example from the Sklearn documentation for K-Folds: And let’s see the result — the folds: As you can see, the function split the original data into different subsets of the data.

Because we would get a big number of training sets (equals to the number of samples), this method is very computationally expensive and should be used on small datasets.

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