Feature manual

The Media Creation Tool also has the ability to install windows without the need of a dvd or flash drive!!! Updated an old got in PC from to today. Took about 2 hours, as it is still running on old HDDs. Tested all commonly used programs. Only one failed, but it is a beta product. The previous version is also installed and still works. Save my name, email, and website in this browser for the next time I comment. Please click on the following link to open the newsletter signup page: Ghacks Newsletter Sign up.

Ghacks is a technology news blog that was founded in by Martin Brinkmann. It has since then become one of the most popular tech news sites on the Internet with five authors and regular contributions from freelance writers. Search for:.

Martin Brinkmann. Windows , Windows How to download and install Windows 10 Feature Updates. Did you know that there are multiple ways to download and install feature updates for Windows 10? Our guide lists all official options. Microsoft Windows Security Updates January overview.

Photos app update for Windows 11 rolls out with a new interface and editing options. Here's how to install the new Media Player in the Windows 11 stable channel. Previous Post: « Microsoft to offer xonly Windows 10 feature updates for businesses Next Post: « Bugs and issues of Windows 10 version Comments Anders said on October 3, at pm.

Malte said on October 3, at pm. Yuliya said on October 3, at pm. Lakas said on October 3, at pm. It just happens to be more compressed. As another example—try it yourself—if the data looks like a two-humped camel and we standardize it, it would still have two humps. Here, we make use of Pandas categorical capabilities to create a coloring value:. The easiest way to keep things fair between values measured on very different scales is to rescale them to an approximately common scale.

Another example occurs when different types of measurements are in the same model. For example, household income is often tens of thousands of dollars—but cars per household is typically measured in single digits.

Numerically, these values have a very different weight. We offset that by placing them on a common scale. Some of the benefits of rescaling become more prominent when we move beyond predictive modeling and start making statistical or causal claims.

It is crucial that these methods be used inside of cross-validation to prevent overfitting on the training data. You can think of sufficiently advanced discretization strategies as micro-learners of their own—imagine if they could discretize right into the correct classification buckets!

We can also do the same discretization using the pd. But, frankly, going to raw np results in a fairly readable solution that involves minimal behind-the-scenes magic. Even though we pushed that computation through, does it make sense to break such well-behaved data right in the middle? Two very similar sepal length values—mean plus a little and mean minus a little—are forced into different buckets.

The big hump also means most of the values are in that region around the mean. Do we want them together or split apart? Only our prediction problem can tell us for sure. If we have domain knowledge, we might have other reasons to pick certain split points. For example, an accountant might introduce split points at tax bracket thresholds.

We can always cross-validate and compare. Which technique should you use? The answer is it depends. You may not be a NumPy ninja, so the last option may not make sense to you. But if you have more complex numerical data, you might get some other wins by using NumPy.

The sklearn method will plug directly into cross-validation using pipelines. However, if you need to do data exploration and processing to come up with the split points, stick with Pandas. But we can rearrange the data and do other learning tasks. For example, imagine we want to predict petal length from the other features, including the species. Now, we take the species as a known input feature and the petal length as an unknown target.

Species does not have a direct numerical interpretation. We could use numbers to represent categories—think class 0, class 1, class 2. However, if we pass this column to linear regression, what would it mean for a species coefficient to be multiplied by these different values? Our general technique for encoding discrete data is called coding categorical variables. It translates a single column with multiple values into multiple columns with one, and only one, on value.

The on value for an example is usually a binary 1 or True with the other column values being 0 or False. Here are the iris species in a one-hot encoding:. Several technical points are worth mentioning here. OneHotEncoder requires numerical inputs. It also requires a 2D input—hence the call to reshape. Finally, if we go one-hot, there are many, many zeros in the result. Remember, only one value per example is turned on over all the expanded columns. So, sklearn is clever and stores the data in a compressed format that deals well with sparsity—a technical term for data with lots of zeros.

Instead of recording values everywhere, it records just the nonzero entries and assumes everything else is zero. There are some learning methods that can work efficiently with sparse data; they know that many values are zero and are smart about not doing extra work. If we want to see the data in its usual, complete form, we have to ask to make it dense.

You can imagine that when we fill out the sparse form, we have a table that is a bit like Swiss cheese: it has lots of holes in it.

We have to fill those holes—values that were assumed to be zero—in with actual zeros. Then we have a solid—dense—table with entries everywhere. We do that with the. We can also perform one-hot encoding with pandas. One benefit is that we can ask it to give nice labels to the one-hot columns. We can merge the one-hot species with the original data for fun and profit. We may want to visualize the relationship between the encoding and the original species values. Here goes:. In the statistical world, the one-hot coding goes by the names of treatment or dummy coding.

My reason for bringing yet another option up is not to overwhelm you with alternatives. In reality, I want to segue into talking about a few useful feature engineering tasks we can do with patsy and also deepen your understanding of the implications of categorical encodings.

Now, I claimed it was a nice system for doing things like one-hot encoding. But, for crying out loud, that previous cell is hideous. We get two of the features coded explicitly and we get a column of all ones under the name Intercept.

So, why do we have to do the -1 to get the simple result? Why does the dmatrix for species give us a column of ones and —seemingly! We were building design matrices— dmatrix critters—with two main elements: 1 some specification of a modeling idea and 2 the data we want to run through that model.

A design matrix tells us how we get from raw data to the form of the data we want to run through the underlying number-crunching of a modeling process. We might specify that we want to predict petal length from petal width and species—our regression twist on the iris data. Treatment '. The C indicates that we want to encode the species before running the linear regression.

Now, I want to investigate what happens with including—or not—certain variable codings. To do so, we need some trivial data we can process in our heads. Here we go:. In this example, the cat costs are 20 and The single dog example has a cost of Make a quick mental note that the average cat cost is After we fit , linear regression will have chosen particular knob values, the ws or m, b.

It is intimately tied to the default dmatrix having a column of all ones and not coding all three species explicitly. A quick comment on interpreting the pet entries. Basically, we pick one of those columns and the result chooses our cost.

You might also notice that the cat value is the average of the two cat cases; the dog value is the single dog value. Retail Spending. View code. Manual vs Automated Feature Engineering Comparison The traditional process of manual feature engineering requires building one feature at a time by hand informed by domain knowledge.

Highlights Featuretools offers us the following benefits: Up to 10x reduction in development time Better predictive performance Interpretable features with real-world significance Fits into existing machine learning pipelines Ensures data is valid in time-series problems Automated feature engineering will change the way you do machine learning by allowing you to develop better predictive models in a fraction of the time as the traditional approach.

Loan Repayment Prediction: Build Better Models Faster Given a dataset of 58 millions rows spread across 7 tables and the task of predicting whether or not a client will default on a loan, Featuretools delivered a better predictive model in a fraction of the time as manual feature engineering.

The features built by Featuretools are also human-intrepretable and can give us insight into the problem: Retail Spending Prediction: Ensure Models Use Valid Data When we have time-series data, we traditionally have to be extremely careful about making sure our model only trains on valid data. Featuretools can take care of time filters automatically , allowing us to focus on other aspects of the machine learning pipeline and delivering better overall predictive models: Engine Life Prediction: Automatically Create Meaningful Features In this problem of predicting how long an engine will run until it fails, we observe that Featuretools creates meaningful features which can inform our thinking about real-world problems as seen in the most important features: Scaling with Dask For examples of how Featuretools can scale - either on a single machine or a cluster - see the Feature Matrix with Dask EntitySet and Featuretools on Dask notebooks.

Feature Labs Featuretools is an open source project created by Feature Labs. Contact Any questions can be directed to help featurelabs.

Releases No releases published. Packages 0 No packages published. Yes No. Thank you! Any more feedback? The more you tell us the more we can help. Can you help us improve? Resolved my issue. Clear instructions.

Easy to follow. No jargon. Pictures helped.