Skip to main content
100%

Gist 1c87e6026e22589f1729

✓ Published0🌍 Public
Aakeshavan
Last edited Oct 23, 2014
Created on Oct 22, 2014

This visualization tracks the misclassification rate of a stochastic gradient descent classifier over 5,000 iterations, with separate lines for training and test datasets. The code uses Python with NumPy, Pandas, and Seaborn’s `color_palette` for styling, while Matplotlib’s `plot` and `legend` functions render the two curves. The y-axis shows misclassification rate and the x-axis shows iteration counter, revealing how the model’s performance evolves during a single pass through the data with a constant step size.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist d8aa62e81e12e25d45a3

This visualization presents statistical calculations for brain imaging data, comparing reliability metrics across different analysis methods. The notebook uses Python with rpy2 to bridge R and Python, calling the `psych` package for intraclass correlation coefficients and `statsmodels` for additional statistical modeling. It generates plots with matplotlib and seaborn, applying custom chart-cleaning functions to remove unnecessary axes and gridlines. The code also leverages pandas for data manipulation and NumPy for z-score normalization, with output rendered inline through IPython’s `%pylab` magic.

Aakeshavan
67% match
Loading thumbnail…

Gist 1ec7d82931c03d4134419af0ccfdd985

This visualization demonstrates a synthetic image-segmentation experiment using TensorFlow and Keras, where a binary mask of a filled circle is generated with the skimage.draw.circle function and displayed via matplotlib. The notebook focuses on computing the Hausdorff distance metric between predicted and ground-truth segmentation masks, using Keras backend operations to implement the distance calculation. The visualization shows the input circle image as a matplotlib axes image, illustrating how the metric is applied to evaluate segmentation quality. The code imports TensorFlow, NumPy, Keras backend, and matplotlib inline, establishing a workflow for comparing segmentation outputs against reference shapes.

Aakeshavan
67% match
Loading thumbnail…

Gist d4b2a37f3ec9a90469c8

This visualization simulates longitudinal brain-imaging measurements across multiple subjects and time points, showing how percentile values (e.g., MTR_25) vary and are modeled with ordinary least squares regression per subject. It first computes subject means, average standard deviations, and true beta coefficients from the input CSV, then generates synthetic data for three time points using normal distributions. The code relies on pandas for data manipulation, statsmodels formula API for OLS fitting, numpy for random sampling, and seaborn for plotting.

Aakeshavan
67% match
Loading thumbnail…

Gist 9540056

This visualization simulates multi-site MRI data, modeling brain volume across two sites for control and patient groups, and compares three regression strategies for combining site data, including a linear calibration transformation. It shows how model predictions and confidence intervals vary across age and disease metrics. Built in a Jupyter notebook, the code uses NumPy for data generation, statsmodels for ordinary least squares and robust linear model fitting, and matplotlib with prettyplotlib for plotting the results.

Aakeshavan
66% match
Loading thumbnail…

Gist 895545c65773a661a6035d5ebc9d1313

This notebook demonstrates a TensorFlow/Keras implementation of the Hausdorff distance metric, visualized with synthetic image data. It uses `skimage.draw.circle` to generate a ground-truth circle image, then computes and displays the Hausdorff distance between predicted and actual binary masks via matplotlib. The visualization includes an inline image plot of the mask, rendered as an interactive matplotlib axes image, alongside the execution of the metric computation within the Keras backend.

Aakeshavan
66% match
Loading thumbnail…

Gist 39800f8b9a6191fff320

This visualization demonstrates the relationship between brain scan variables and subject metadata across a multi-site study. It uses pandas for data loading, seaborn for statistical plotting, and matplotlib for inline rendering within a Jupyter notebook. The code reads a FreeSurfer CSV dataset, applies LeaveOneOut cross-validation from scikit-learn, and fits models with statsmodels formula API. It displays sample rows of the dataframe to show the structure of the neuroimaging data.

Aakeshavan
66% match
Loading thumbnail…

Gist 98636bb57280e38492740cdbe9c4901c

This example demonstrates a deep learning workflow for classifying brain images using TensorFlow and Keras. It loads CSV data from the braindr dataset, displaying a pandas DataFrame with vote counts, rater identities, and mean ratings for each image. The code builds a convolutional neural network with layers like Conv2D, MaxPooling2D, Dropout, and LeakyReLU, using Keras' Sequential API. It configures the model for GPU execution, imports optimizers such as RMSprop and Adam, and prepares callbacks like ModelCheckpoint for training.

Aakeshavan
65% match
Loading thumbnail…

Gist fd5980c016748347a3ed

This visualization displays correlation plots of spinal cord measurements from a multiple sclerosis dataset, including metrics like cord area, gray matter, and white matter at different spinal levels, alongside clinical scores (EDSS). The analysis uses pandas to load the data and seaborn to generate the plots, with a white background, Set1 color palette, and talk-context styling. Dark red and dark blue colors distinguish the plots, which examine relationships between structural measurements and disability scores.

Aakeshavan
65% match