Example #5Source File: mcg_munge.py From Collaborative-Learning-for-Weakly-Supervised-Object-Detection with MIT License 6 votes def munge(src_dir): # stored as: ./MCG-COCO-val2014-boxes/COCO_val2014_000000193401.mat # want: ./MCG/mat/COCO_val2014_0/COCO_val2014_000000141/COCO_val2014_...
Consider for example a supervised system that tries to predict traffic levels in a city as a function of Location+Time. In this case, trying to learn trends that vary by seconds would mostly be misleading. The year wouldn’t add much value to the model as well. Hours, day and month ...
you'll cover recipes on using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors, and generate visualisations for exploratory data analysis (EDA) to visualise unexpected values.Finally, you'll build functions and classes that you can reuse without modifi...
For example, if we have two categorical features, 'Gender' and 'Marital Status,' we can create a new feature, 'Gender-Marital Status,' to capture the interaction between the two features. This can help to capture non-linear relationships between the features and the target variable. Binning ...
In this example, over the days, the diagnosis of a patient may have gone from a cold to the flu. Try using several combinations of labels and colors to see what you can discover. Let's analyze the data points in more detail by defining the binning of the x axis and y axis. Defining...
PiML also works for arbitrary supervised ML models under regression and binary classification settings. It supports a whole spectrum of outcome testing, including but not limited to the following: Accuracy: popular metrics like MSE, MAE for regression tasks and ACC, AUC, Recall, Precision, F1-sco...
We will look at what needs to be done with a dataset before analysis takes place, such as removing duplicates, replacing values, renaming axis indexes, discretization and binning, and detecting and filtering outliers. We will work on transforming data using a function or mapping, permutation, ...
Additionally, efficient data structures can be used to represent the binning of the input data; for example, histograms can be used and the tree construction algorithm can be further tailored for the efficient use of histograms in the construction of each tree. These techniques were originally deve...
Difference between supervised and reinforcement learning Applications of reinforcement learning Unified machine learning workflow Data preprocessing Data collection Data analysis Data cleaning normalization and transformation Data preparation Training sets and corpus creation Model creation and training Model evaluation...
Having no ticks at all can be useful in many situations—for example, when you want to show a grid of images. For instance, consider Figure 4-75, which includes images of different faces, an example often used in supervised machine learning problems (for more information, see “In-Depth:...