Cocalc Tutorial 09 Ipynb

Let's look at the avocado data, which we looked at in week 3, and try to use the small hass volumes of avocados to predict their large hass volumes. To reduce the size of the dataset, let's also narrow our observations to only include avocados from 2015. We can measure the quality of our regression model using the RMSPE value—just like how we used accuracy to evaluate our knn classification models. In the readings, we looked at both RMSE and RMSPE and their differences. RMSE refers to the root mean squared error, or predicting and evaluating prediction quality on the training data. RMSPE refers to the root mean squared prediction error, or the error in our predictions made about the actual testing data.

We look at this property when we evaluate the quality of our final predictions. Recognize situations where a simple regression analysis would be appropriate for making predictions. Explain the k-nearest neighbour (k-nn) regression algorithm and describe how it differs from k-nn classification. Interpret the output of a k-nn regression. In a dataset with two variables, perform k-nearest neighbour regression in R using caret::train() to predict the values for a test dataset. Using R, execute cross-validation in R to choose the number of neighbours.

Let's look at the avocado data, which we looked at in week 3, and try to use the small hass volumes of avocados to predict their large hass volumes. To reduce the size of the dataset, let's also narrow our observations to only include avocados from 2015. We can measure the quality of our regression model using the RMSPE value—just like how we used accuracy to evaluate our knn classification models. In the readings, we looked at both RMSE and RMSPE and their differences. RMSE refers to the root mean squared error, or predicting and evaluating prediction quality on the training data. RMSPE refers to the root mean squared prediction error, or the error in our predictions made about the actual testing data.

We look at this property when we evaluate the quality of our final predictions. Watch the following video for a general introduction to integer programming. Decision variables constrained to integer values Can produce 5 or 6 cars, but not 5.72 cars For pure integer programming (IP) problems, solutions can be obtained simply by changing the domain for the LP from NonNegativeReals to PositiveIntegers in the Pyomo coding (as seen in textbook problem 3.4-10 as a... Computationally, integer programming can be much more difficult than linear programming (this post can help you visualize why this is so)

Originally published at https://sagemath.org/calctut and adapted as interactive worksheets on CoCalc. 01-review.ipynb: review of basics (trigonometry, ...) Computers read data, as we saw in notebooks 1 and 2. We can then build functions that model that data to make decisions, as we saw in notebooks 3 and 5. But how do you make sure that the model actually fits the data well? In the last notebook, we saw that we can fiddle with the parameters of our function defining the model to reduce the loss function.

However, we don't want to have to pick the model parameters ourselves. Choosing parameters ourselves works well enough when we have a simple model and only a few data points, but can quickly become extremely complex for more detailed models and larger data sets. Instead, we want our machine to learn the parameters that fit the model to our data, without needing us to fiddle with the parameters ourselves. In this notebook, we'll talk about the "learning" in machine learning. Let's go back to our example of fitting parameters from notebook 3. Recall that we looked at whether the amount of green in the pictures could distinguish between an apple and a banana, and used a sigmoid function to model our choice of "apple or banana"...

Intuitively, how did you tweak the sliders so that way the model sends apples to 0 and bananas to 1? Most likely, you did the following: Assume the following situation. From an experiment we have gathered following data: We want to use the data as an input to a simulation. However, as visible, the data is noisy and thus may lead to instability of our simulation. First we will load modules supporting this tutorial.

Note that you should install matplotlib first if not already happenend, as only this tutorial needs matplotlib. For usage of ebcpy, you don't need it. Let's specify the path to our measurement data and load it. If you're familiar with python and DataFrames, you will ask yourself: Why do I need the TimeSeriesData-Class? We implemented this class to combine the powerful pandas.DataFrame class with new functions for an easy usage in the context of Building Energy Systems for three main reasons: Most data in our case is Time-Dependent, therefore functions for easy conversion between seconds (for simulation) and Timestamps (for measurements) is needed

All material moved to the more comprehensive CoCalc Manual CoCalc is a cloud-based service that provides infrastructure and services that are useful for running courses based on Jupyter Notebooks. It is used for teaching by Universities around the world. All material moved to the more comprehensive CoCalc Manual For a list of authors see the contributors section. 📚 The CoCalc Library - books, templates and other resources

The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book! No changes were made to the contents of this notebook from the original. < Sorting Arrays | Contents | Data Manipulation with Pandas > While often our data can be well represented by a homogeneous array of values, sometimes this is not the case. This section demonstrates the use of NumPy's structured arrays and record arrays, which provide efficient storage for compound, heterogeneous data.

While the patterns shown here are useful for simple operations, scenarios like this often lend themselves to the use of Pandas Dataframes, which we'll explore in Chapter 3.

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