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H: Is there any reason why providing symbolic features into an MLP wouldn't outperform feeding raw pixels to a CNN in a RL task?
I am tackling a RL problem (relaxed version of Space Fortress) with DQN. The usual approach would be to feed pixels into a CNN but that is usually very slow. I am considering feeding symboli... |
H: Package/function to convert the word to one hot vector in Python NLP
Is there a package or function in NLP which can be used to convert the word into an one hot vector.
Thank you.
AI: There are several ways to convert words to one hot encoded vectors. Since I do not know the data structure you store your data. I as... |
H: Size of folds in k-fold cross-validation
When evaluating results using cross-validation, several strategies can be adopted, as using 5 or 10 folds, or doing leave one out cross-validation, as well as doing a 80/20 split.
Under which are general conditions should I try one or another?
AI: I generally advocate for cr... |
H: Keras VAE example loss function
The code here:
https://github.com/keras-team/keras/blob/master/examples/variational_autoencoder.py
Specifically line 53:
xent_loss = original_dim * metrics.binary_crossentropy(x, x_decoded_mean)
Why is the cross entropy multiplied by original_dim? Also, does this function calculate c... |
H: Why is cross-validation score so low?
I am using Scikit-Learn for this classification problem. The dataset has 3 features and 600 data points with labels.
First I used Nearest Neighbor classifier. Instead of using cross-validation, I manually run the fit 5 times and everytime resplit the dataset (80-20) to trainin... |
H: De-normalization in Linear Regression
I have implemented a Linear regression model on a dataset of 7 independent variable and 1 target with the below 2 approaches
1) Without normalization of the data, resulted in a Mean squared error of 36530921.0123 and R2 value 0.7477
2) With normalization of the data, resulted... |
H: Space between an object and the ground truth bounding box
Which way is better for drawing the ground truth boxes for object detection?
Drawing as tight as the sides of the box touch the border of the object, or
Make a little space between the box and the object?
AI: The first image is a better bounding box. A pe... |
H: Should I update my regularisation L1 and L2 regularisation parameters in online setting?
I have been working on online learning for a few weeks now, especially with Vowpal Wabbit and logistic regression. My understanding of the online learning algorithms and the problem is alright but I can't get my head straight a... |
H: Stochastic Gradient Descent Batching
I'm new to regression and we are doing a very simple exercise in a course. I'm taking to get a basic understanding of GD and SGD for a linear regression.
From my understanding, the only difference between GD and SGD is that instead of performing the algorithm on dataset size m a... |
H: When to clean data?
I am very new to data science / ML and I have what I think is a very basic question - when to 'clean' the data?
Do I clean data before using it to train a classifier (a binary classifier in my experiments)?
Do I clean data that I try to classify using this classifer?
Both?
The data in my case ... |
H: Which Loss cross-entropy do I've to use?
I'm working with this dataset https://www.kaggle.com/c/sf-crime to predict the crime incident using keras. I've encoded the category with pd.get_dummies and then use it as the validation data. At first I try to use categorical_crossentropy and adam for the loss function but ... |
H: Implementation of Stochastic Gradient Descent in Python
I am attempting to implement a basic Stochastic Gradient Descent algorithm for a 2-d linear regression in Python. I was given some boilerplate code for vanilla GD, and I have attempted to convert it to work for SGD.
Specifically -- I am a little unsure as to i... |
H: Predicting customers purchase
Suppose we have a list of products categorized into 10 categories. We also have customers order details such as
order_id, product_name, quantity, order_date
We want to know for a particular month what are the most probable categories from which customer might buy products. How can we... |
H: Linear Regression. Interpret weights as an effect on a prediction?
In a basic linear regression, can I use the weights of each explanatory variable to describe their relative effects on the predicted value? If parameter A has a weight of 100, and parameter B has a weight of 10, can I say that parameter A has 10 tim... |
H: Data Visualisation for Dataframe having 3 columns
I have the below data, I am looking for a effective visualization or graphical method (in Python) to understand how the err is distributed with respect to min and max.
Data_X_err.head()
Out[137]:
min max err
0 35435 35933 1.40
1 35155 36382 3.43
2 ... |
H: Grad Checking, verify by average?
I am running Gradient Checking, to spot any discrepancy between my mathematically-computed gradient and the actual sampled gradient - to reassure my backprop was implemented correctly.
When computing such a discrepancy, can I sum-up squares of differences, then take their average?... |
H: How to find the ranges in Equal frequency/depth binning?
I have been looking into the site http://www.saedsayad.com/unsupervised_binning.htm and there it shows range values to the right under equal frequency binning ....
I have so much looked to find how the ranges are formed but has been in vain. So , can somebody... |
H: How to use the same minmaxscaler used on the training data with new data?
Im using the keras LSTM model to make prediction, and the code above is to scale the data:
inputs are shaped like (n, 11, 1) and the label is 1D
DailyDemand.py
#scaling data
scaler_x = preprocessing.MinMaxScaler(feature_range =(-1, 1))
x = ... |
H: Very low accuracy of new data compared to validation data
I'm trying to train the neural network to predict the movement of a particular security on the market.
I teach on historical data collected for the year. At the entrance of the neural network candlesticks are served: close price and value
Before submitting, ... |
H: Image resizing and padding for CNN
I want to train a CNN for image recognition. Images for training have not fixed size. I want the input size for the CNN to be 50x100 (height x width), for example. When I resize some small sized images (for example 32x32) to input size, the content of the image is stretched horizo... |
H: Default value of mtry for random forests
It is argued that the default value of mtry for random forests is square root of total number of features (for classification) and number of features divided by 3 for regression. Can someone tell me the literature where it is specifically mentioned?
AI: The textbook 'The Ele... |
H: Using handcrafted features in CNN
What is the difference between using CNN with handcrafted features and CNN without handcrafted features?
Thank you
AI: A CNN automatically extracts features, so hand-crafting features has become unnecessary for most applications. It learns what features to extract via backpropagat... |
H: Class distribution discrepancy training/validation. Loss now uninterpretable?
I have a 3-class image classification problem. The classes are highly unbalanced (with about 98% of the images beloning to one class). To counteract this unbalanced data, I weight the losses by using the inverse of the class probabilities... |
H: Improve test accuracy for TensorFlow CNN
I'm trying to use Tensorflow for signal classification. The signals are either normal or high-risk signals. For this purpose, I used convolutional neural networks. The length of signals are 685 and the architecture is:
Convolution layer with 27 channels and 1 by 16 window s... |
H: GradientChecking, can I blame float precision?
I am trying to GradientCheck my c++ LSTM. The structure is as follows:
output vector (5D)
Dense Layer with softmax (5 neurons)
LSTM layer (5 neurons)
input vector (5D)
My gradient check uses this formula
$$d = \frac{\vert \vert (g-n) \vert \vert _2 }{
\vert \vert g \... |
H: Ideal aggregation function for Partially Connected Neural Network (PCNN)
I am building a Python library that creates Partially Connected Neural Networks based on input and output data (X,Y). The basic gist is that the network graph is arbitrarily updated with nodes and edges in the hidden layers.
Example Data:
X = ... |
H: When is precision more important over recall?
Can anyone give me some examples where precision is important and some examples where recall is important?
AI: For rare cancer data modeling, anything that doesn't account for false-negatives is a crime. Recall is a better measure than precision.
For YouTube recommendat... |
H: Do I need a next for loop in order to get all values?
When i print this in the module without printing to file, it prints all values. But when I print it to the designated file, it only prints the first value.
#Create the model base
from sklearn.feature_selection import chi2
import numpy as np
N = 2
for UNSPSC, ca... |
H: Neural network using Tensorflow
Is there any possibility that cost function might end up in local minima rather than global minima while implementing the Neural network using Tensorflow ?
AI: Most of the critical points in a neural network are not local minima, as it can be seen in this question. Although it is not... |
H: What does "baseline" mean in the context of machine learning?
What does "baseline" mean in the context of machine learning and data science?
Someone wrote me:
Hint: An appropriate baseline will give an RMSE of approximately 200.
I don't get this. Does he mean that if my predictive model on the training data has a... |
H: Accuracy and loss don't change in CNN. Is it over-fitting?
My task is to perform classify news articles as Interesting [1] or Uninteresting [0].
My training set has 4053 articles out of which 179 are Interesting. The validation set has 664 articles out of which 17 are Interesting.
I have preprocessed the articles a... |
H: WEKA Multilayer Perceptron with large dataset
I'm new to data mining using WEKA.
I was trying out datasets with a large dataset (2000+ attributes with 90 instances) and left the default parameters as it is.
Why is Multilayer Perceptron running long on a dataset with 2000+ attributes?
K-Nearest Neighbour does a bet... |
H: The goal of fine tuning
I would like to ask what is the goal of fine-tuning a VGGNet on my dataset.
What does fine-tuning mean? Does it mean to change the weights or keep the weight values?
AI: Fine tuning means changing the weights such that the VGGNet can perform the task you want in your dataset. The reason why ... |
H: Why does Ensemble Averaging actually improve results?
Why does ensemble averaging work for neural networks? This is the main idea behind things like dropout.
Consider an example of a hypersurface defined by the following image (white means lowest Cost). We have two networks: yellow and red, each network has 2 weigh... |
H: Binarized neural network
I am currently looking at a paper by Hubara et al on binarized neural network.
I am stuck in understanding algorithm 2 of the paper. The algorithm uses shift-based (bit-shifting) AdaMax, where AdaMax is an extension of the Adam optimizer. In particular, they are using
$$m_{t} = \beta_{1}... |
H: Trade off between Bias and Variance
What are the best ideas or approaches to trade off between bias and variance in Machine Learning models.
AI: You want to decide this based on how well your model performs and generalizes. If your model is underfitting, you want to increase your model's complexity, increasing vari... |
H: which NN should I use for Time-series dataset, whose pattern change as time goes
I am analyzing a time-series dataset using (supervised) tensorflow deep learning. The tensorflow code is given a series of inputs, and based on each input, the NN has to predict output value in near future.
For training there are lots ... |
H: Removing irrelevant content in columns
I have a dataset with a column named Package Size and it has data in two units inches and meters mentioned in words. How do I preprocess the data such that I remove the unnecessary word and also convert them into a single unit?
Example:
Package Size 13 inches4 meters
Pack... |
H: How do I get a count of values based on custom bucket-ranges I create for a select column in dataframe?
I have a column in my dataset by name 'production_output' whose values are integers in the range of 2.300000e+01 to 1.110055e+08.
I want to arbitrarily split the values in this column into different buckets based... |
H: Feature Selection in Linear Regression
I have a insurance dataset as given below. For which I need to build a model to calculate the charges.
age sex bmi children smoker region charges
0 19 female 27.900 0 yes southwest 16884.92400
1 18 male 33.770 1 no south... |
H: What does Logits in machine learning mean?
"One common mistake that I would make is adding a non-linearity to my logits output."
What does the term "logit" means here or what does it represent ?
AI: Logits interpreted to be the unnormalised (or not-yet normalised) predictions (or outputs) of a model. These can give... |
H: Pros/Cons of stop word removal?
What are the pros / cons of removing stop words from text in the context of a text classification problem, I'm wondering what the best approach is (i.e. to remove or not to remove)?
I've read somewhere (but can't locate the reference) that it may be detrimental the the performance of... |
H: Advantages of one shot learning over image classification
This is a rather conceptual question. From what I've read I gather that one shot learning is useful for use cases in which you don't have datasets of millions of images of employees etc. By a one shot operation, the network or networks will do a similarity m... |
H: ggplot aes() choice
What is the difference between
ggplot(mtcars, aes(mpg)) +
geom_histogram(aes(y = ..density..))
and
ggplot(mtcars, aes(mpg), aes(y = ..density..)) +
geom_histogram()
I know that aes() i the geom layer overrides the aes() in the data layer. But are one of the code snippets above preferable?... |
H: Predict method of the perceptron algorithm
Can someone explain to me how the predict method of the perceptron algorithm works?
def predict(self, pattern):
activation = np.dot(self.w, pattern) + self.b
if activation > 0:
return 1
else:
return -1
In this example b stands for bias.
def t... |
H: BOVW - Combine vocabularies
What I have so far
I have a set of images that I am trying to classify. I can also extract different feature descriptors from the images using algorithms such as hu moments, color histogram, and SIFT. I can the build a vocabularies from each of these algorithms.
What I do not understand ... |
H: Pretraining neural net example in Aurelien Geron's book
I am testing the pretraining example in Chapter 15 of Aurélien Géron's book "Hands-On Machine Learning with Scikit-Learn and TensorFlow". The code is on his github page: here - see the example in section "Unsupervised pretraining".
Pretraining the network with... |
H: Why Root Finding is important in Logistic Regression? (i.e. Newton Raphson)
I'd like to ask what is the main reason why we find the roots in logistic regression (i.e. why we use Newton Raphson method on logistic regression ). I understand the basics of Newton Raphson method, but I just can't understand what is the ... |
H: Validation showing huge fluctuations. What could be the cause?
I'm training a CNN for a 3-class image classification problem. My training loss decreased smoothly, which is the expected behaviour. However, my validation loss shows a lot of fluctuation.
Is this something that I should be worried about, or should I j... |
H: Boruta Feature Selection package
I am using Boruta feature selection (with Random forest) to decide the important features in the below data set.
Gender Married Dependents Education Self_Employed ApplicantIncome \
0 Male No 0 Graduate No 5849
1 Male Yes ... |
H: Why is there a big drop off in performance in my GBM?
I'm working on an employee attrition predictive model using sklearn's GradientBoostingClassfier. I have 9,000 observations, which I split 50/50 for training and testing. I have another set of 1,200 observations that I use for a final validation. All 10,200 obser... |
H: Ratio between embedded vector dimensions and vocabulary size
Using Embedding layer in Keras on a fairly small vocabulary (~300), I am looking at how to choose the output of this layer (dense vector) when given a 300 dimension vector. I think that the embedded vector need to have a minimum length to be able to map ... |
H: np.c_ converts data type to object. Can I prevent that?
Was trying my hand at the Titanic dataset, when I wanted to One Hot Encode a categorical feature, after which I wanted to combine the original data with the new one hot vectors. The datatypes are as such:
data : Pandas Dataframe
Titles_ohe : Numpy sparse matri... |
H: One hot encoding at character level with Keras
I am reading Chollet's book on deep learning at the moment and in the NLP chapter he says:
Note that Keras has built-in utilities for doing one-hot encoding of text at the word level or character
I have looked into Keras metods and I cannot find which function he is re... |
H: What is difference between a Data Scientist and a Data Analyst?
https://www.datacamp.com/community/tutorials/learn-data-science-infographic
https://www.datacamp.com/community/blog/data-engineering-vs-data-science-infographic
These links contain almost everything but not the difference between data science and data ... |
H: How to represent relation between users as a feature?
I'm developing a model for unsupervised anomaly detection. I have a dataset representing communications between users (each example represents a communication): there are many features (time, duration, ...) and the ids of sender and receiver. My question is: how... |
H: how to derive the steps of principal component analysis?
I'm trying to understand the theoretical reasoning behind the method, but I can't understand a particular step in the middle of this page.
"The constraint on the numbers in $v_1$ is that the sum of the squares of the coefficients equals $1$. Expressed mathema... |
H: why is mse training drastically different from the begining of each training with Encoder-Decoder
I am using encoder-decoder model to predict binary images from grayscale images. Here is the model
inputs = Input(shape=(height, width, 1))
conv1 = Conv2D(4, 3, activation = 'relu', padding = 'same', kernel_initialize... |
H: Skewness in response variable
Why is it a problem that your response variable is skewed in regression? Is taking logarithms the only way to solve it?
AI: Taking logarithms is not a one-size-fits-all approach - what if your response is negatively skewed?
"Simple" linear regression assumes that the response variable ... |
H: Creating labels for Text classification using keras
I have a text file with information that needs to classified based on keywords. The text file contains many number of paragraphs. And the paragraph contains keywords that we want (lets say salary amount, interest rate and so on..)
I want to write a model which wil... |
H: Number of Fully connected layers in standard CNNs
I have a question targeting some basics of CNN. I came across various CNN networks like AlexNet, GoogLeNet and LeNet.
I read at a lot of places that AlexNet has 3 Fully Connected layers with 4096, 4096, 1000 layers each. The layer containing 1000 nodes is the classi... |
H: How to optimize for time correlated hidden function - the magical candy machine
Let's assume that we have this magical candy machine which takes candies as input and delivers again candies as output.
For any given time t, it picks a random function which is strictly
increasing up to a point such as f(2) = 6 here, ... |
H: Why not use Scaler.fit_transform on total dataframe?
In sklearn I'm normalizing the data with MinMaxScaler. The example I'm following uses
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
X_train, X_test, y_train, y_test = train_test_split(X_crime, y_crime,random_state = 0)
X_train_scaled =... |
H: Why is spam detection a classification problem and not a class modelling problem
Trying to get my feet wet with machine learning on text.
The most common dataset I've seen in this space is the sms dataset with classes ham and spam.
And the most common and successful approach seems to be to model this as a binary c... |
H: How to release datasets with fingerprinting
I intend on monetising some large datasets. These datasets are anonymised and released to (paying) clients via a web api. Are there any standard algorithms such that if the datasets are intentionally leaked publicly, the data can be altered such that the responsible party... |
H: How to Normalize & Scale a Single Data Point
I do understand the concept of normalizing & scaling the training/test data; it does help with the converging of the cost function. It is a great helper for many of the machine learning algorithms.
I train and validate my model with the normalized data (MinMaxScaler) an... |
H: Evaluation methods for multi-class classification
I am looking for single-number evaluation method that can be used in multi-class classification tasks that take into account imbalanced data-sets. For instance, ROC-AUC defined by binary classifiers, is a single-number and takes into account imbalanced data-sets. On... |
H: Help me choose a Data Science book in Python
I've been a Data Scientist for a few years now, but I've only recently started to do most of my work in Python (boy, do I miss ggplot2! But altair is coming to the rescue).
I want to improve my Python skills and, since most of my work is related to developing Data Scien... |
H: How to improve accuracy of deep neural networks
I am using Tensorflow to predict whether the given sentence is positive and negative. I have take 5000 samples of positive sentences and 5000 samples of negative sentences. 90% of the data I used it for training the neural network and rest 10% for testing.
Below is th... |
H: custom delimiter for dat file
I've a dat file in the following format which I'm trying to load using pandas.
28::Persuasion (1995)::Romance
29::City of Lost Children, The (1995)::Adventure|Sci-Fi
30::Shanghai Triad (Yao a yao yao dao waipo qiao) (1995)::Drama
31::Dangerous Minds (1995)::Drama
32::Twelve Monkeys (19... |
H: What is normalization for?
I am new in python and data science (and not great in math). I am learning machine learning. I got following normalize function. Can you please explain what does this normalize function do?
def normalize(array):
return (array - array.mean()) / array.std()
Also please explain what thi... |
H: autoEncoder as LSTM input, any benefit?
When training my LSTM, is there any incentive to pre-pass its inputs through an auto-encoder, or should I always supply as raw data as possible?
I have is a small amount of training data, and it contains only a few very rare significant events (spikes, spaced out in time)
AI:... |
H: GridSearch mean_test_score vs mean_train_score
I am working with scikit learn and GridSearch in order to find the best parameters in my classifiers.
I have a map of different hyperparameters and I want to print out GridSearch results, but I do not understand one thing - what is the difference between mean_test_sco... |
H: MemoryError for np.array
I was trying the Keras CNN Stater Code on Ubuntu 16.04, from the below link:
https://www.hackerearth.com/challenge/competitive/deep-learning-3/machine-learning/predict-the-energy-used-612632a9/#c144537
I get “MemoryError:” for
X_train = np.array(train_img, np.float32) / 255.
Any idea, what... |
H: Training with data of different shapes. Is padding an alternative?
I have a dataset of about 1k samples and I want to apply some unsuspervised techniques in order to clustering and visualization of these data.
The data can be interpreted as a table of a spreadsheet and unfortunately it doensn't have a very defined ... |
H: Calculate average Intersection over Union
I want to have a global IoU metric for each class in a segmentation model with a neural net. The idea is, once the net is trained, doing the forward pass over all training examples an calculate the IoU, I'm thinking in two approaches (for each class):
1) Calculate IoU for e... |
H: What does 1024 by 3 model mean?
I was watching this video and Sentdex mentioned he had to switch around 1024 by 3 model. What does he mean by 1024 by 3 model and what did he change around?
Edited: Youtube link with the timestamp where he talks about this topic
AI: At 3:30 in video, he mentions this switching. Wh... |
H: Caret and rpart - does caret automatically prune rpart trees
Question relating to the caret package 'rpart' method.
Does the method='rpart' automatically prune the tree? If so, what rules does it follow? If not, how does one go about directing caret to do this?
AI: To give a proper background for rpart package an... |
H: Understanding scipy sparse matrix types
I am trying to select the best scipy sparse matrix type to use in my algorithm. In that, I should initialize data in a vij way, then I should use it to perform matrix vector multiplication. Eventually I have to add rows and cols. Trying to select the best for my problem, I wa... |
H: Ridge and Lasso Regularization
Recently, I started working on Ridge and Lasso regularization for Linear and Logistic Regression.
My doubts are given below:
Is the penalty the same (by same proportion) for all the coefficients or is it based on variable importance?
If it is the latter I believe we can directly app... |
H: Linear Regression - finding thetha using Normal equation
This is to find thetha which will give minimum cost function. Why is the x0 column required? why cant we assign size as x0? why do we need the feature count to be n+1?
AI: Consider the 1-D equivalent of the table you have provided. In this case, you have one... |
H: What is the input space of a neural network (or other supervised learning algorithms)?
While training the neural network (or any other supervised learning algorithms), we supply input variables and corresponding outputs. The input variables can be continuous or discrete (binary in many cases).
What happens if afte... |
H: Why is a correlation matrix symmetric?
I'm sorry for being so weak in math. (I'm a student) For eg. this is a correlation matrix.
Q1 Q2 Q3
Q1 1.000000 0.707568 0.014746
Q2 0.707568 1.000000 -0.039130
Q3 0.014746 -0.039130 1.000000
Why is it symmetric? Why is Q1:Q2, the same as Q2:Q1?... |
H: Question about K-Fold Cross Validation
In a machine learning procedure, suppose we've chosen k=10 for the "K-Fold Cross Validation".
After we do the k steps of "K-Fold Cross Validation", how do we choose the final model for the classifier ? (The one will we use in order to predict new data)
AI: K-fold cross validat... |
H: Is there any alternative to L-BFGS-B algorithm for hyperparameter optimization in Scikit learn?
The Gaussian process regression can be computed in scikit learn using an object of class GaussianProcessRegressor as:
gp= GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
kernel=1**2 + Matern(length_scale=2,... |
H: How to build a data analysis pipeline procedure
I have a series of scripts. Some are in R, some in Python, and others in SAS. I have built them in such a way that one code outputs a .csv file that the next code obtains and then that code outputs a .csv file, and so on...
I want to create a script that will automat... |
H: tree.DecisionTree.feature_importances_ Numbers correspond to how features?
clf = tree.DecisionTreeClassifier(random_state = 0)
clf = clf.fit(X_train, y_train)
importances = clf.feature_importances_
importances variable is an array consisting of numbers that represent the importance of the variables. I wonder what ... |
H: Detecting text region from an image
So I'm working on a document processing AI and I already have a character recognition model which performs decently well. Now the problem is, how do I feed each character to the model in order to make predictions. Sliding window is one technique to segment arbitrary sequences of ... |
H: R vs. Python Decision Tree
From my experiences the R Decision tree returns more accurate results than the python decision tree. Can anymore confirm this assumption and maybe knows the reason?
AI: Decision trees involve a lot of hyperparameters -
min / max samples in each leaf/leaves
size
depth of tree
criteria ... |
H: Handling outliers and Null values in Decision tree
Outliers : As I understand, decision trees are robust to outliers. Can anybody please confirm if my hypothesis is right with an example?
(What if I have a features ranging from 0 to 9 but there is an outlier of which value is 10000?)
Whether it creates a separate l... |
H: Are Hadoop and Python SciPy used for the same?
Right now
I've been doing a bit of research because I'm quite new to Big Data world. Among several other tools or frameworks, I've read about Apache Hadoop and Python for data analysis.
Specifically, I've read that:
Hadoop: allows you to perform any task you want, if i... |
H: Handling Concat and Shift Feature in Pandas for Data Science
I am trying to USE Lag features AND Concat() and Shift() function,
seies = Series.from_csv('sugar_price_2.csv', header=0, sep=';')
In [25]: prices = DataFrame(series.values)
In [26]: dataframe = concat([prices.shift(3), prices.shift(2), pri... |
H: The differences between SVM and Logistic Regression
I am reading about SVM and I've faced to the point that non-kernelized SVMs are nothing more than linear separators. Therefore, is the only difference between an SVM and logistic regression the criterium to choose the boundary?
Apparently, SVM chooses the maximum ... |
H: Is 10,000 images for one class of spectrograms good enough for music classification model?
I'm debating on using a DNN or CNN for this classification, but I have 10,000 spectrograms of songs. I was wondering if I need more because the images have a low variants.
AI: Agreed with Emre. One thing that can be helpful w... |
H: Evaluation metrics for Decision Tree regressor and KNN regressor
I have started working on the Decision Tree Regressor and KNN Regressor.
I have built the model and not sure what are the metrics needs to be considered for evaluation. As of now I have considered Root mean squared error.
Can we use $R^2$ values for... |
H: Confusion-matrix clarification from Python
confusion_matrix(y_test1, pred)
That is my codes
Confusion matrix, without normalization
True [[724258 438]
value [ 25396 302]]
Predicted value
I understand that is how this is the confusion matrix. But I do not know the order of classification re... |
H: Next best predictions in decision tree
I am using decision tree classifier to predict some block selected based on the below data.
I am able to predict the "Block selected" column based on the data. How to get the second best, third best prediction and so on(I need a ordered list)? Can I get this using decision tr... |
H: GAN - why doesn't the generator nullify the noise input?
In GAN architecture, during training, what keeps the generator's output dependant on the input noise? Why don't the weights of the noise input become zero (plus a bias)?
I would expect the generator to converge to outputting a single picture which is extremel... |
H: Question about Knn and split validation
I have a big database with 40k recors and 2 classification classes. In this big database the 76% of records belong to the first class.
I've used a 70-30 split partition with stratified sampling, and the K-nn gives the best accuracy on k = 20.
1) Is it too big value for k ?
2)... |
H: Is this the correct way to apply a recommender system based on KNN and cosine similarity to predict continuous values?
My data is:
userID, gameID, rating (1.0 through 10.0)
First, I normalize the values the ratings of each row.
I use cosine similarity to create a similarity matrix where each cell represents similar... |
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