What is Model Stacking?

Model Stacking is a way to improve model predictions by combining the outputs of multiple models and running them through another machine learning model called a meta-learner. It is a popular strategy used to win kaggle competitions, but despite their usefulness they’re rarely talked about in data science articles — which I hope to change.

Essentially a stacked model works by running the output of multiple models through a “meta-learner” (usually a linear regressor/classifier, but can be other models like decision trees). The meta-learner attempts to minimize the weakness and maximize the strengths of every individual model. …

From project planning, architectural design, my technical challenges, and the machine learning model’s results

Photo Credit: Toby Gotesman Schneier

About The Shelter

Family Promise is a non-profit organization that provides shelter to homeless families. The work they do is of tremendous value to the community.

In 2019, the shelter provided services to more than 110,000 men, women, and children. They housed nearly 20,000 people — most of whom were children. Since the organization’s inception they’ve housed more than a million people.

With the help of the organization’s services, most of these families return to stable housing and get out of poverty.

In order to achieve higher efficiency in helping families in need, the organization looked for ways to better optimize their day-to-day…

1) What are Random Forest Models?

Random Forest models are a form of machine learning that, generally speaking, produces great results with very little hyper-parameter tuning.

The idea behind them is actually incredibly simple. Basically, Random Forest Models are a collection of decision trees that ask “questions” about the data to predict values.

The above decision tree is a model I’ve generated using stock and economic data. The top label represents the question being asked (basically asking if a value is larger or smaller than a certain number) and depending on the answer you move either left or right down the decision tree. …

In this article, I will be doing statistical analysis on NYSE firms to see if we can find any patterns between company fundamentals and their share prices.

I will use a data set from Kaggle, which provides information on share prices, business fundamentals, and sector classifications.

I thought a good starting point for this article would be to see if we can use fundamentals data to describe why certain market sectors outperform others. As we can see from the table below, there is a sharp distinction between the best-performing sector (healthcare) and the worst-performing sector (energy). …

Trevor Pedersen

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