If it was supervised, we would have pairs of photos of the same streets, in sunny & rainy weather. But such data is hard to come by, especially in the quantities needed for deep learning. So what if you just have a bunch of sunny street photos, and a set of rainy ones? (with no...
Binning Implemented binning methods are: Histogram Binning for classification [3], [4] and object detection [12] (netcal.binning.HistogramBinning) Isotonic Regression [4],[5] (netcal.binning.IsotonicRegression) Bayesian Binning into Quantiles (BBQ) [1] (netcal.binning.BBQ) Ensemble of Near Isoton...
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...
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...
Classification Understanding unsupervised learning Applications of unsupervised learning Clustering using MiniBatch K-means clustering Extracting keywords Plotting clusters Word cloud Understanding reinforcement learning Difference between supervised and reinforcement learning Applications of reinforcement learning Unified ...
SciPy is a scientific library for Python and is open source. We are going to use this library in the upcoming chapters. This library depends on the NumPy library, which provides an efficient n-dimensional array manipulation function. We are going to learn more about these libraries in the upc...
TheK-nearest Neighbors (KNN)algorithm is a type of supervised machine learning algorithm used for classification, regression as well as outlier detection. It is extremely easy to implement in its most basic form but can perform fairly complex tasks. It is a lazy learning algorithm since it doesn...
This can be achieved by discretization or binning values into a fixed number of buckets. This can reduce the number of unique values for each feature from tens of thousands down to a few hundred. This allows the decision tree to operate upon the ordinal bucket (an integer) instead of ...
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, ...
Michael Walker has worked as a data analyst for over 30 years at a variety of educational institutions. He is currently the CIO at College Unbound in Providence, Rhode Island, in the United States. He has also taught data science, research methods, statistics, and computer programming to under...