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With an abundance of machine-learning models that grows every single day, it can be extremely difficult to focus and hone your skills into the right models and algorithms. Whether your target is continuous, categorical, or something else, it can be incredibly difficult to pull a single fish out of the lake of models.
Although there a millions to choose from, there are certainly a select few that I hold near and dear to my heart, and subjectively I really love. Though I’d definitely prefect not to have bias (data-science joke,) I will admit, It’s really difficult not to give these amazing models preferential treatment!
The Multinomial Naive Bayes (MultinomialNB) model is a fantastic model for targets of the categorical specification. MultinomialNB is well-known for its use in big-data-pipelines from the likes of Google, Microsoft, and Yahoo. In my experience this model is one of the greatest models you can use for booleans with tokenized text, making MultinomialNB’s claim to fame anything involving NLP classification. Whether you’re guessing who is most likely to say a famous quote, or determining if text is spam, MultinomialNB is definitely an essential model for data-scientists working on Flask HTTP pipelines, and algorithms to sort user text queries.
A regression tree is the love-child of a Decision Tree and Linear Regression. Regression Trees typically use a correlation coefficient to determine an ample model to fit in order to boost accuracy. This model is ideal for continuous data with steep curves and high variance. Regression Trees can be similar to isotonic regression, with a key difference of using the correlation coefficient rather than isotonic weight.
These are my subjective favorite models, and the ones that I find myself turning to most often. There is a tool for every job just as there is a model for every job, just as there are features for every job. With time comes the wonderful Data Science Superpower that is knowing the best way to implement which model. A great understanding can come from a multitude of sources, but as a lifetime learner, never expect to get too comfortable with one model or another.
I’m moderately guilty of this in some circumstances, because it can be comforting to return to what you know, but traveling to the dark depths of stack overflow is likely the greatest education you can get for Data Science. Among many other things, a strong knowledge of Machine Learning models will come soon after. And when you start receiving accuracy scores of ninety-two percent on your first go-round, you’ll certainly know that you’re doing something right!
These are my subjective favorite models, and the ones that I find myself turning to most often. There is a tool for every job just as there is a model for every job, just as there are features for every job. With time comes the wonderful Data Science Superpower that is knowing the best way to implement which model. A great understanding can come from a multitude of sources, but as a lifetime learner, never expect to get too comfortable with one model or another.
These are my subjective favorite models, and the ones that I find myself turning to most often. There is a tool for every job just as there is a model for every job, just as there are features for every job. With time comes the wonderful Data Science Superpower that is knowing the best way to.
Gradient Descent is a broad and extremely versatile model. This is the model that works behind the scenes on nearly all Neural Networks. There are some Bayesian models that aren’t necessarily considered Gradient Descent, but most often they still hold their roots in Gradient Descent. For the present and future of AI, there’s a pretty big chance that Gradient Descent will be the most important model. Gradient Descent is capable of anything from simple Feed Forward Networks all the way to Deep Recurrent Neural Networks, and beyond.
Anytime you see machine-learning being used to manipulate images, Gradient Descent is certainly the culprate. Needless to say, we all should know Gradient Descent; not only is it super cool, but it’s also super fun!
These are my subjective favorite models, and the ones that I find myself turning to most often. There is a tool for every job just as there is a model for every job, just as there are features for every job. With time comes the wonderful Data Science Superpower that is knowing the best way to implement which model. A great understanding can come from a multitude of sources, but as a lifetime learner, never expect to get too comfortable with one model or another. I’m moderately guilty of this in some circumstances, because it can be comforting to return to what you know, but traveling to the dark depths of stack overflow is likely the greatest education you can get for Data Science. Among many other things, a strong knowledge of Machine Learning models will come soon after. And when you start receiving accuracy scores of ninety-two percent on your first go-round, you’ll certainly know that you’re doing something right!
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I love that Data Science can flip your entire ideology on its head with one glob of information. One day you can be working on creating paintings with a Gradient Descent model, and the next thing you know you’re on Wikipedia reading about some obscure regression algorithm. Data Science is always changing, and the models with it, so your favorite models are certain to migrate!
This new model was very interesting, and somewhat game changing because it generally invited the idea of stochastic discrimination into the machine-learning world. Prior to the random forest model, all classification algorithms were correlation based, Bayesian, or based on probability distributions on occassions, for example, Binomial Distribution.
| Title 1 | Title 2 | Title 2 |
|---|---|---|
| col 3 is | right-aligned | $1600 |
| col 2 is | centered | $12 |
| zebra stripes | are neat | $1 |
I love that Data Science can flip your entire ideology on its head with one glob of information. One day you can be working on creating paintings with a Gradient Descent model, and the next thing you know you’re on Wikipedia reading about some obscure regression algorithm. Data Science is always changing, and the models with it, so your favorite models are certain to migrate!