I have a heatmap image (correlation between all matrix columns) and I'm straggling to preform all the changes below within the same image: bar colors should be symmetric around zero (e.g., correlation of 1 and -1 should be with the same color) change the correlation matrix to a diagonal matrix, since correlation values are symmetric - and show only upper matrix triangle (mask out the lower triangle ) show the correlation values in every cell of the diagonal matrix x,y …
To provide a full yet simple picture of a 3-level, one-way ANOVA, I use the following visualization where variation within each group (the filled circles) and variation between the groups (black arrows) are simple to be understood. But I'm wondering if it could be possible to extend the current visualization to a 2 x 3 two-way ANOVA (adding another way with two groups to the current visualization)? (Note: the dashed vertical lines denote each group's mean)
I am doing a lot of work with transfer learning at the moment (using keras and tensorflow if that is relevant). I am having a lot of issues in sufficiently summarizing the very large models. This post: How do you visualize neural network architectures? shows a lot of useful methods for visualizing architectures, and they are great for networks such VGG16, but none of them are reasonable to include in a report if the models are very large (such as …
When writing a paper / making a presentation about a topic which is about neural networks, one usually visualizes the networks architecture. What are good / simple ways to visualize common architectures automatically?
The result of my computational simulation is a (time-dependent) system of large number (~100k) of moving points in a confined space. Each point has its own Cartesian coordinates as well as a weight (w) in the form of $(x_i,y_i,z_i;w_i)$. I'm looking for a software/app/package to create a snapshot of the 3D spatial density map of these points. (something like this). Like you see in this figure, the points are not going to be displayed individually, but only a transparent cloud …
I am trying to create a Choropleth map using the Chorolpeth widget from the Geo Add-on in Orange. However, this widget is not appearing? Any ideas? My current version: Orange version 3.24.1 for Windows
ColorBrewer seems to be very useful in selecting a color pallet to represent factors that have up to 12 possible values. I have 28. Is it a horrible idea to represent 28 variables with color? If so, could you suggest an alternative visual indicator? Currently I'm using the colors for column side colors in a heatmap shown below. As you can see, the Strain column is not very informative:
I have created and analyzed around 16 machine learning models using WEKA. Right now, I have a CSV file which shows the models' metrics (such as percent_correct, F-measure, recall, precision, etc.). I am trying to conduct a (modified) student's t-test on these models. I am able to conduct one (according to THIS link) where I compare only ONE variable common to only TWO models. I want to perform a (or multiple) t-tests with MULTIPLE variables and MULTIPLE models at once. …
I'm working on CRM data, did some cleaning, encoding and ran a decision tree classifier from which i plotted a feature_importance graph From that I found that Sales person column is one of the important feature which is highly cardinal column(around 1300+ categories/sales person). Now i'm trying to generate some insights on this column with respect to target column(binary values). Would like to know in general how to create insights from such a large categorical column? P.S: Other columns are …
I'm trying to visualise a neural network schematic and found a great tool for building schematics here http://alexlenail.me/NN-SVG/index.html. I've edited the SVG file to change one of the dense layers into a LSTM layer, and the input to time series instead of singular neurons. At the bottom of the image there is some set notation detailing how many neurons is in each layer. I'm not too familiar with set notation. I'm not quite sure how to represent the LSTM layers …
from scipy.sparse import hstack X_tr1 = hstack((X_train_cc_ohe, X_train_csc_ohe, X_train_grade_ohe, X_train_price_norm, X_train_tnppp_norm, X_train_essay_bow, X_train_pt_bow)).tocsr() X_te1 = hstack((X_test_cc_ohe, X_test_csc_ohe, X_test_grade_ohe, X_test_price_norm, X_test_tnppp_norm, X_test_essay_bow, X_test_pt_bow)).tocsr() X_train_cc_ohe and all are vectorized categorical data, and X_train_pt_bow is bag of words vectorized text data. Now, I applied a decision tree classifier on this model and got this: I took max_depth as 3 just for visualization purposes. My question is: I would like to get feature names in my output instead of index as X2599, X4 etc. …
I've clusterized my data according to 3 criteria in 3 groups. I used kmeans to obtain those cluster so the label for each cluster is random and changes at each script run. To evaluate the consistency of my clusters I decided to use Jaccard index but I can't understand how to apply it properly. Let's say I have this data where alpha beta and gamma are the 3 methods, and the Cluster Index is the value returned by K-means for …
What is the most convenient way to visualize Softmax values after calling the CNN prediction function? Do I have to collect different probability values and feed them to the matplotlib or are there any more convenient ways/libraries to do this? Below is one example what I mean:
cat = {'A':1, 'B':2, 'C':3} dog = {'A':2, 'B':2, 'C':4} owl = {'A':3, 'B':3, 'C':3} Suppose I have 3 dictionary, each containing pairs of (subcategory, count). How can I plot a segmented bar chart (i.e stacked bar graph) using Python with x being 3 categories (cat, dog, owl) and y being proportion (of each subcategory)? What I have in mind looks like this:
I have used 4 machine learning models on a task and now I am struggling to plot their bar charts just like shown below in the image. I am printing classification report to get precision, recall etc. My code is shown: def Statistics(data): # Classification Report print("Classification Report is shown below") print(classification_report(data['actual labels'],data['predicted labels'])) # Confusion matrix print("Confusion matrix is shown below") cm=confusion_matrix(data['actual labels'],data['predicted labels']) plt.figure(figsize=(10,7)) sn.heatmap(cm, annot=True,cmap='Blues', fmt='g') plt.xlabel('Predicted') plt.ylabel('Truth') Statistics(data) How can I plot this type of chart …
For example, the SVM or ANN methods perform search of a surface which would separate the data points in a best way. This surface is returned in the vector or parametric form. Are there methods returning a spatial bitmap each voxel of which contains a numeric value defining a class for all points lying within a given voxel? I would like to share some of the results of my attempts in this direction. Since I'm relatively new in machine learning …
I am trying to visualize data using R and scatterplot3d. I have loaded data and used: colors <- c("#999999", "#E69F00", "#56B4E9" ) scatterplot3d(output$X2,output$X6 , output$X7 , color=colors, pch="X9") X9 is label column in my dataset. it contains 3 categories : A , B , C. By documentation: color : colors of points in the plot, optional if x is an appropriate structure. Will be ignored if highlight.3d = TRUE. pch: plotting "character", i.e. symbol to use. Yet I still get …
I'm new to DS. I want to perform EDA on such dataset, where these are the missing features stats of my train and test sets: train: Test_0 0 Test_1 31 Test_2 0 Test_3 141 Test_4 0 Test_5 0 Test_6 0 Test_7 0 Test_8 1045 Test_9 0 Test_10 0 Test_11 0 Test_12 0 Test_13 0 Test_14 0 Test_15 2967 Class 0 dtype: int64 test: Test_0 0 Test_1 7 Test_2 0 Test_3 46 Test_4 0 Test_5 0 Test_6 0 Test_7 0 Test_8 …
I have a bunch of plots as the one reported below. The data is from measurements performed on different times and different days. In the plot (which is a cumulative distribution function, if that matters), the colors differentiate data relevant to different days; the markers are used to further differentiate the data within each day. The problem is that the plot is very crowded and a bit ugly. Some markers can be barely seen. Question: Any idea how I can …