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H: is it possible to do feature selection for unsupervised machine learning problems?
I started looking for ways to do feature selection in machine learning.
By having a quick look at this post , I made the assumption that feature selection is only manageable for supervised learning problems:
Still, I have to ask: ... |
H: Python - Todoist API - Store data into Dictionary
I've this Python code that allows me to collect data from Todoist API. Now I want to store this data into a Dictionary in order to make some data processing and some advanced analytics. Using this code how can I store this values into the structure ?
from pytodoist ... |
H: Does classification of a balanced data-set lead to any problem?
So I came across a bioinformatics paper, where I found a line which says:
One potential problem with using a training set with
equal numbers of positive and negative examples in cross-validation
is that it can artificially inflate performance esti... |
H: What is Big Data?
I want to know what is Big Data? Can I have practical example.
How big data can be? I need numbers where big data term is applicable. If you can provide link for case study with actual numbers, with reference to V's of Big Data.
AI: If I want to quote from Wikipedia, Big data is data sets that are... |
H: Python clustering and labels
i'm currently experimenting with scikit and the DBSCAN algorithm. And i'm wondering how to combine the data with the labels to write them into a new file. I'd also like to understand how the labels array is used to filter the examples.
Please correct me anytime i say something wrong be... |
H: When Should I Use ggplot2 Instead of Tableau?
I am a student getting started with Tableau for the first time. My proficiency with ggplot2 is intermediate. I can create custom versions of the most popular kinds of charts in ggplot2 but nothing too fancy (and not very time-efficiently).
I am interested in creating m... |
H: How do I convert strings in CSV into integer in Pandas?
for my supervised classification problem,
I have a train dataset which contains past purchase data of customers and 5 new products are purchased by these customers. I have a test dataset which contains past purchase data of customers. They never bought from t... |
H: How do machine learning models (e.g. neural networks) get better over time from new data?
I'm a complete newbie to the world of machine learning, and I'm currently working on implementing a model that will need to incorporate feedback, as well as changes to the data set (both feature & label changes over time).
The... |
H: Markov Chains for sequential data
I am new to Markov chains and HMM and I am looking for help in developing a program (in python) that predicts the next state based on 20 previous states (lets say 20 states in last 20 months). I have a sequential dataset with 50 customers i.e. the rows contain sequence of 20 state... |
H: Image Matching to solve captcha
I am building a bot with python and I need some system to solve captchas like these:
I think I need a deep learning algorithm, but coding one is a pain in the ass. Is there any easy solution to this?
I can code the part that screenshots the images and clicks on the correct answer, i... |
H: How to count occurrences of values within specific range by row
I have a data frame of 3000 rows x 101 columns like as follow:
Time id0 id1 id2 ………… id99
1 1.71 6.99 4.01 ………… 4.98
2 1.72 6.78 3.15 ………… 4.97
.
.
3000 0.36 0.23 0.14 ………… 0.28
Using Python, how could we add a column... |
H: sagemath: compared to r.quantile, what is a faster way to find boundaries for a boxplot?
I was using the r.quantile method in sagemath to find boundaries for a box plot.
The plot was taking a long time using r.quantile.
r.quantile took more than 20 seconds to find the quartiles for a data set that could be sorted a... |
H: How to set batch_size, steps_per epoch, and validation steps?
I am starting to learn CNNs using Keras. I am using the theano backend.
I don't understand how to set values to:
batch_size
steps_per_epoch
validation_steps
What should be the value set to batch_size, steps_per_epoch, and validation_steps, if I have 24... |
H: Supervised learning for variable length feature-less data
I have data in following form:
a: 1,2,3,2,3
a: 2,4,5,6,7,8,0,9,7,6,5,6,2
a: 7,8,9,3,4
b: 4,5,3,5,6,3,5,1,2
b: 1,6,3,2,4,5
b: 2,4,5,6,7,8,0,9,7,6,5,6,2
c: 7,8,9,3,4
c: 4,5,3,5,6,3,5,1,2
...
(in reality, each case has about 100-200 numbers, though the length... |
H: Data Matching Using Machine Learning
I have around 4000 customer records and 6000 user records and about 3000 customer records match leaving 1000 unmatched customers. I have created a fuzzy matching algorithm using Levenshtein and Hamming and added weights to certain properties, but I want to be able to match the r... |
H: Should I fit my parameters with brute force
I am running analysis on data for this type of sensor my company makes. I want to quantify the health of the sensor based on three features using the following formula:
sensor health index = feature1 * A + feature2 * B + feature3 *C
We also need to pick a threshold so tha... |
H: benchmark Result for MovieLens dataset?
I am looking for a benchmark result or any kaggle competition held using MovieLens(20M or latest) dataset.
Similar question has been asked here but, provided links are dead so re-raising the question.
AI: One result for MovieLens 20M using Factorization Machine can be found h... |
H: How to apply the gradient of softmax in backprop
I recently did a homework where I had to learn a model for the MNIST 10-digit classification. The HW had some scaffolding code and I was supposed to work in the context of this code.
My homework works / passes tests but now I'm trying to do it all from scratch (my ow... |
H: Visualizing software metrics
I have the below sets of data per application, you can call them as software metrics. These metrics vary depending on the size of an application.
Bugs
CodeSmells
Vulnerability
The size of the application is determined by LOC (Lines of code), how can i showcase the complexity of each ap... |
H: How to compare two regression models?
Which measurement(s) should one choose to compare two regression models?
After modifying a learning algorithm(specifically, a regression algorithm, let's call it M1) to generate another learning algorithm M2, how to validate if the above modification is efficient?
here is what ... |
H: Cannot get the prediction right using Stochastic Gradient Descent: Always predicts 1
I have a CSV file with 20 columns and 785 rows. The 785th row for each column is a label describing the encoded image. The encoded image is either 3 or 5. So 1-784 row is the encoded image and 785th row is the label that names the ... |
H: What are the benefits of having ML in js?
What are the benefits of having ML in JavaScript I.e. the deeplearn.js (now tensorflow) stuff, as opposed to implementing the ML steps in a python backend?
AI: There are a lot of services that offer free or very cheap hosting of static websites. If you are able to implement... |
H: Create new data frames from existing data frame based on unique column values
I have a large data set (4.5 million rows, 35 columns). The columns of interest are company_id (string) and company_score (float). There are approximately 10,000 unique company_id's.
company_id company_score date_submitted comp... |
H: Difference between Time series clustering and Time series Segmentation
In the context of time series data mining, I have read about time series segmentation and time series clustering, but I couldn't differentiate between both. In case they are different, how these methods are correlated with each other?
Well from ... |
H: How can I draw bar graph in python on aggregated data?
Normally when I draw bar plot its simple as
import matplotlib.pyplot as plt
from pylab import rcParams
import seaborn as sb
%matplotlib inline
rcParams['figure.figsize'] = 5, 4
sb.set_style('whitegrid')
x = range(1, 10)
y = [1,2,3,4,0.5,4,3,2,1]
plt.bar(x, ... |
H: Are there any python libraries for sequences clustering?
I have a problem which I explained in other question. I've understood that my dataset is a sequence of states or something like that. Is there libraries to analyze sequence with python? And is it right way to use Hidden Markov Models to cluster sequences?
AI:... |
H: One Hot Encoding vs Word Embedding - When to choose one or another?
A colleague of mine is having an interesting situation, he has quite a large set of possibilities for a defined categorical feature (+/- 300 different values)
The usual data science approach would be to perform a One-Hot Encoding.
However, wouldn't... |
H: Visualizing Decision Tree of K-Nearest-Neighbours classifier
I'm using Sklearn's KNN to build a classifier and was wondering if there is any way to visualize the decision tree that the algorithm builds. Maybe something of this fashion
AI: $k$-NN does not build a decision tree to classify a new instance, it looks at... |
H: Instead of one-hot encoding a categorical variable, could I profile the data and use the percentile value from it's cumulative density distribution?
I have a categorical variable which has thousands of values, for a dataset which has millions of records. The data is being used to create a binary classification mode... |
H: XGBoost equations (for dummies)
I am having a hard time trying to understand the MSE loss function given in the Introduction to Boosted Trees (beware! My maths skills are the equivalent of a very sparse matrix):
$
\begin{split}\text{obj}^{(t)} & = \sum_{i=1}^n (y_i - (\hat{y}_i^{(t-1)} + f_t(x_i)))^2 + \sum_{i=1}^t... |
H: High model accuracy vs very low validation accuarcy
I'm building a sentiment analysis program in python using Keras Sequential model for deep learning
my data is 20,000 tweets:
positive tweets: 9152 tweets
negative tweets: 10849 tweets
I wrote a sequential model script to make the binary classification as follows... |
H: Censored output data, which activation function for the output layer and which loss function to use?
I am building a neural network to solve a regression problem. The output is a single numerical value.
Unfortunately, the output is censored: the values below 0 were recorded as 0, and postive values remained unchang... |
H: Generalization of Correlation Coefficient
The correlation coefficient tells me how two variables (sequences of numbers) are correlated with each other. Does it generalize to non-linear scenarios? How could one more generally measure the general predictive power of x over y when the relationship between x and y is n... |
H: Pandas grouped data to Bokeh graph
I'm having trouble graphing Pandas grouped data in Bokeh.
company_id company_score date_submitted company_region
AA .07 1/1/2017 NW
AB .08 1/2/2017 NE
CD .0003 1/18/2017 NW
I'... |
H: How does a Q algorithm consider future rewards?
I am trying to understand the underlying logic of Q learning (deep Q learning to be precise). At the moment I am stuck at the notion of future rewards.
To understand the logic, I am reviewing some of the present code samples. This one seemed quite interesting, so I we... |
H: How do I determine if variables are correlated? Is it simply a mathematical calculation?
I'm self learning data science so bear with me as I try and make my question as clear as possible. Lets assume I have a dataset of a dependent variable y and some independent variables x1, x2 and x3 etc.
Is the correlation bet... |
H: Detecting over fitting of SVM/SVC
I am using 3-fold cross validation and a grid search of the C and gamma parameters for a SVC using the RBF kernel I have achieved a classification score of 84%.
When testing against live data the accuracy rate is 70% (1500 samples used). However, when testing against an un-seen hol... |
H: When inputting image rgb values to MLP, should I divide by 255?
I have an MLP with 3072 input nodes which are for 1024 rgb pixels. My datasets is in an array with each row representing one image and looking like this:
[red_pix1, red_pix2, ..., red_pix1024, green_pix1, green_pix2, ..., green_pix1024, blue_pix1, blue... |
H: Hyperparameter Tuning in Machine Learning
What is the difference between Hyper-parameter Tuning and k-NN algorithm? Is k-NN also a type of Hyper-parameter tuning?
AI: In kNN algorithm, you only try to find a suitable value of parameter k. And some models may have many parameters that can be modified. Normal paramet... |
H: Significant overfitting with CV
I working on a binary classification task. The dataset is quite small ~1800 rows and ~60 columns. There are no duplicates in the rows. I am comparing different classifiers amongst the canonical ones: random forest, logistic regression, boosted tree and SVC. I am training the hyperpar... |
H: Help Interpreting a Active Learning Learning Curve
I am applying a active learning with a SGDClassifier (log loss function) as the base learner on some data and I have the following graphs representing the learning curve of queries vs error rate. The green is the validation error and blue is the training error.
Is... |
H: Weights in neural network
So I am newbie in deep learning, I came across activation functions which gives an output and compares it to label, if it's wrong, it adjusts its weight until it gives the same output as labelled data for that particular input in training data set.
x1 x2 x3 y
... |
H: german gunning fog index function
I would like to analyse some text and most of my Reviews are german. Does anyone know if python has a good gunning fog index function for german language? I couldnt find anything
best regards
AI: This might be rather in the field of linguistics than related to data science, but nev... |
H: Convert Atypical Date Format for Time Series in Python
I have an atypical time format that I need to convert into a datetime index for time series analysis. I'm working in Python / Pandas.
The column is 'BC_DT', and the format is "27-MAR-18". Example is below.
BC_DT
27-MAR-18
28-MAR-18
29-MAR-18
I tried this met... |
H: How do I find the minimum value of $x^2+y^2$ with a genetic algorithm?
I want to find $(x,y)$ which minimizes $x^2+y^2$ with GA to apply it for another function.
Does anyone know any example of GA with deap (Python) like that?
AI: Genetic algorithm
This consists in 4 crucial steps: initialization, evaluation, selec... |
H: Is data partitioning necessary for an explanatory model and why?
I've come accross the following paragraph in the To Explain or To Predict? paper by Galit Shmueli.
In explanatory modeling, data partitioning is less common [than in predictive modeling] because of
the reduction in statistical power. When used, it ... |
H: what actually word embedding dimensions values represent?
I am learning word2vec and word embedding , I have downloaded GloVe pre-trained word embedding (shape 40,000 x 50) and using this function to extract information from that:
import numpy as np
def loadGloveModel(gloveFile):
print ("Loading Glove Model")
... |
H: Examples on kohonen self organizing maps
Is there a simple example to start with for using kohonen 1.1.2 or is it only the test file that will be the reference?
AI: There are alternative solutions for self organizing maps.
Best of them I found pymvpa where the example is easy to read and understand. It is also main... |
H: What plt.subplots() doing here?
Below is the code I am trying to execute.
x = np.random.normal(size=1000)
fig, ax = plt.subplots()
H = ax.hist(x, bins=50, alpha=0.5, histtype='stepfilled')
Can anybody elaborate what fig, ax = plt.subplots() doing here ?
Thank you.
AI: As you can read from here
plt.subplots() is a... |
H: twitter data analysis?
I am involved in twitter analysis data. I want to find trending topics in tweets with some hashtags, like #finance or #technology. I have a hugh data set of tweets and now I need to analyze them.
I need to recognize topics, if there are. They way I'm approaching this is, first, performing a... |
H: Which is the best Machine learning technique for this Load forecasting problem?
I am trying to use Machine Learning to predict the load of a residence at any point in time for a whole year. I have past data pertaining to that house. So I have the training data and I need the algorithm to predict future loads of the... |
H: Do DBMS decrease Memory requirements?
I finished my Economics thesis using RStudio, but my script was very slow due to massive RAM consumption during the process.
My Case
I had a massive dataset (stock prices in daily frequency for 10 years, ~700 stocks i.e. $3500\times700$) and I was picking each stock as a vecto... |
H: Euclidean vs. cosine similarity
I have a text dataset which I vectorize using a tfidf technique and now in order to make a cluster analysis I am measuring distances between these vector representations. I have found that a common technique is to measure distance using cosine similarity, and when I ask why euclidean... |
H: Understanding Contrastive Divergence
I’m trying to understand, and eventually build a Restricted Boltzmann Machine. I understand that the update rule - that is the algorithm used to change the weights - is something called “contrastive divergence”. I looked this up on Wikipedia and found these steps:
Take a traini... |
H: How does the number of trees effect the prediction time in gradient boost classification trees?
After tuning hyper-parameters for a gradient boosted model, I have found that the best tree count (iterations) is a few thousand.
I'm worried that such a high count might impact prediction performance.
Can someone expla... |
H: What is one hot encoding in tensorflow?
I am currently doing a course in tensorflow in which they used tf.one_hot(indices, depth). Now I don't understand how these indices change into that binary sequence.
Can somebody please explain to me the exact process???
AI: Suppose you have a categorical feature in your dat... |
H: How to learn from multiple data sources with different input variables but the same underlying pattern?
I will explain with an example:
Let's say you have 2 factories that produce pulp paper. Each have similar processes where the laws of physics give the same outcome. Now let's say this 2 factories have equipment a... |
H: Question on bias-variance tradeoff and means of optimization
So I was wondering how does one, for example, can best optimize the model they are trying to build when confronted with issues presented by high bias or high variance. Now, of course, you can play with the regularization parameter to get to a satisfying e... |
H: How to delete entire row if values in a column are NaN
I'd like to drop all the rows containing a NaN values pertaining to a column. Lets assume I have a dataset like this:
Age Height Weight Gender
12 5'7 NaN M
NaN 5'8 160 M
32 5'5 165 NaN
21 NaN 155 F
55 5'10 170... |
H: Is there a way to replace existing values with NaN
I'm experimenting with the algorithms in iPython Notebooks and would like to know if I can replace the existing values in a dataset with Nan (about 50% or more) at random positions with each column having different proportions of Nan values.
I'm using the Iris data... |
H: How can I transpose a high dimensional dataset?
I have a (.csv) file with more than 35,000 rows and 100 columns, where the rows represent the attributes and columns represent the instances. In Excel the maximum number of columns is 16,384. Therefore, it is impossible to do this in one single Excel sheet. I need my ... |
H: Right ML mode and metric to minimize FN and FP on imbalanced dataset
So I have a dataset in which I have to predict class binary label (1 or 0), the problem, out of 120k data points, only 200 have the label '1'.
the aim is to minimize FN and FP.
Which ML model should I use? Gradient boost or XGBoost or logistic r... |
H: Why not always use the ADAM optimization technique?
It seems the Adaptive Moment Estimation (Adam) optimizer nearly always works better (faster and more reliably reaching a global minimum) when minimising the cost function in training neural nets.
Why not always use Adam? Why even bother using RMSProp or momentum o... |
H: How to load a csv file into [Pandas] dataframe if computer runs out of RAM?
I have been trying to train a neural network, but my computer is always running out of RAM memory when I'm loading the dataframe with Pandas. Its a .csv file that is like 7+ GB.
I wanted to try some primitive batching but in order to one ho... |
H: Standardization and Normalization
Which and all Machine Learning algorithms needs the data to be standardised/normalised before feeding into the model. How do we determine whether the particular model/data needs to be standardised/normalised.
Thank you.
AI: Whenever you have features that they have different scale... |
H: Is there a quick way to check for multicollinearity between categorical variables in R?
I have a large amount of categorical and dummy variables (36) and I would like to remove a number of them based on their multicollinearity (or just collinearity). Instead of using Chi Square tests over and over again, are there ... |
H: How to predict customer's next purchase
Suppose we want to predict what customer will buy during his next visit to the Electronic Shop based on his past purchase history. I know that it is a very broad question, but I am new to machine learning and don't have much idea about how to approach this problem.
The simple... |
H: Questions on ensemble technique in machine learning
I am studying the ensemble machine learning and when I read some articles online, I encountered 2 questions.
1.
In this article, it mentions
Instead, model 2 may have a better overall performance on all the data
points, but it has worse performance on the very ... |
H: What is difference between detrend and normalization?
matlab function detrend subtracts the mean from data. If data contains several data columns, detrend treats each data column separately.
One of the normalization technique is subtracting the mean and dividing it by standard deviation.
Since the normalization a... |
H: What if my validation set is worse than my training?
I am running a CNN, on the 1st epoch my training set accuracy is 15% and validation set is 12%, by the 51st epoch my training accuracy is 87% and validation set is 13%. What is happening? What does it mean if my validation set is lower or greater than my training... |
H: How to pass common inputs to a merged model in Keras
I'm attempting to merge a VGG-16 and ResNet-50 through concatenation. I was successful in training and saving the merged model. Here is the code snippet:
from keras import applications
from keras.models import Model
from keras.layers import Dense, GlobalAveragePo... |
H: Linear Regression in Python
Below is the dataset for which I am trying to implement Linear regression in
python.
age sex bmi children smoker region charges
0 19 female 27.900 0 yes southwest 16884.92400
1 18 male 33.770 1 no southeast 1725.55230
2 28 ... |
H: Is there t-SNE in WEKA?
I want to use t-SNE in WEKA just for visualization purposes. I tried to look at the package manager but could not find any implementation of it.
Is there anything that I can do to achieve it?
AI: Sadly no, there is not a T-SNE implementation for WEKA.
If you can install python packages in yo... |
H: Formatting categories of data with pandas in Python
I have around 9500 cities that I want to put in a pandas dataframe, then store in a file for later use. For example, below, these are 3 cities that I have data for. Some range in size based on year. Lovell_Wyoming only has 9 years of data points, corresponding to ... |
H: Basic classification question
I am wondering how I can manage a test data after using PCA or normalization and another thing like that in the classification because our model works on the representation given by its input vectors. For example, suppose you have used PCA in your training dataset to gain better accura... |
H: Can you interpolate with QLearning or Reinforcement learning in general?
I am currently researching the usages of machine learning paradigms for pathfinding problems. I am currently looking into the reinforcement learning paradigm and I used QLearning for pathfinding.
When there are not many states QLearning seems... |
H: How does ,the Mutlinomial Bayes's alpha parameter, affects the text classification task?
I would like to know how the alpha parameter, in Multinomial Bayes, affects the text classification task.
I know that this parameter is correlated to the algorithm's ability in classifying unseen words during training. How chan... |
H: Stacked time series plot in python
In pandas I can set the date as index, and then run df.plot() to see a line chart. How do I make that line chart stacked as in the picture below?
AI: You can simply use
df.plot.area()
Found here after a quick google search. |
H: Tips to improve Linear Regression model
I have just run a Linear regression model on the Dataset having 7 independent variable and 1 target variable. Below is the R squared and MSE values.
Mean squared error for training set : 36530921.0123
$R^2$ value for training set : 0.7477
Can anybody please give me some tip... |
H: What are the state-of-the-art models for identifying objects in photos?
From my observations and little experience it appears that most of the ML project are about classifying stuff. Is there cancer signs on the photo? Does the picture show car, whale or banana? Etc.
I need to implement a model for face identifica... |
H: Handling categorical variables in large df
I have a df with nearly 40 million rows and ~20 columns (total size is 2.2+GB). 15 of my features are categorical. I figured that the most reasonable way to go about this problem without making the df any bigger would be fit/transform each with LabelEncoder and then conver... |
H: Choice of time series models
I built a model for time series in order to forecast new values.
What is the best way to choose the correct model?
Are better criteria like AIC and BIC or the comparison between prediction errors? In this last case I have to split dataset in train and test while in the first case is no... |
H: Use of Random Forest algorithm in PySpark for imputation
I am wondering how to use Random Forest algorithm for imputing missing values in a dataset. It is supposed to work well with missing values but I am not sure how those missing values are dealt with and how RF imputation works in PySpark.
AI: You can do the fo... |
H: General methods outlier detection
What are general methods for outlier detection that do not assume any underlying distribution in the data? I have a dataset with the prizes of the rents in London, as well as their location, number of bedrooms, living rooms and bathrooms. I want to identify outliers in this data, w... |
H: How does backpropagation differ from reverse-mode autodiff
Going through this book, I am familiar with the following:
For each training instance the backpropagation algorithm first makes a
prediction (forward pass), measures the error, then goes through each
layer in reverse to measure the error contribution f... |
H: Regularization - Combine drop out with early stopping
I'm building a RNN (recurrent neural network) with LSTM cells. I'm using time series to perform anomaly detection.
When training my RNN I'm using a dropout of 0.5 and I'm early stopping with a patience of 5 epochs when my validation loss is increasing.
Does it m... |
H: RF and DT overfitting
I am new with Machine Learning and I started with some lessons in Kaggle. There, I learnt how to use DecisionTreeRegressor() and RandomForestRegressor() from sklearn.
However, I cannot really understand how I can verify that my explanatory variables do not overfit the model. For example, the ... |
H: What can functional programming be used for in data science?
In my next academic year at university, I have the option to take a course in Advanced Functional Programming. A basic description of the course is this:
"You’ll focus on a number of more advanced functional programming topics such as: programming with e... |
H: Sparse Matrix - Effect and Solution
Can anybody explain me what are the effects on the model if we have sparse data in our dataset. And also how to deal these sparse matrices ?
Thank you.
AI: The idea is really simple, just look at some online resources like https://en.m.wikipedia.org/wiki/Sparse_matrix
The impleme... |
H: Dropout in other machine learning models
Dropout is a widely used technique in deep learning. Dropout was built for neural networks, but I wonder if other prediction models can use this idea as well as a regularizer. Do you know of any similar technique in linear regression, SVMs or tree-based methods?
AI: Random f... |
H: Why does momentum need learning rate?
If momentum optimizer independently keeps a custom "inertia" value for each weight, then why do we ever need to bother with learning rate?
Surely, momentum would catch up its magnutude pretty quickly to any needed value anyway, why to bother scaling it with learning rate?
$$v_... |
H: Dropout vs weight decay
Dropout and weight decay are both regularization techniques. From my experience, dropout has been more widely used in the last few years. Are there scenarios where weight decay shines more than dropout?
AI: These techniques are not mutually exclusive; combining dropout with weight decay has ... |
H: Neural Network Hidden Layer Selection
I am trying to build an MLP classifier model on a dataset containing 30000 samples and 23 features.
What are the standards I need to consider while selecting the number of hidden layers and number of nodes in each hidden layer?
AI: First try a simple model: The input layer and... |
H: Python Sklearn TfidfVectorizer Feature not matching; delete?
I trained a classifier using TfidfVectorizer in Sklearn. I then pickled the model for future use.
The new x_test that I want to make predictions on, has more features than the x_train from the model. This is the resulting error:
ValueError: X has 4877 fea... |
H: What should be the ratio of True vs False cases in a binary classifier dataset?
I am using a CNN for sentiment analysis of news articles. It is a binary classification with outputs: Interesting & Uninteresting.
In my dataset, there are around 50,000 Uninteresting articles and only about 200 Interesting articles. I ... |
H: How to do Feature Scaling for these ranges [0,1] and [-1,1]?
I want to rescale the features of my data to be between [0,1] and [-1,1]? Is their a clear cut way that works every time for these ranges? I think the below equation works for [0,1] but when it is describe people say generally it works, so I am not certai... |
H: What are the consequences of not freezing layers in transfer learning?
I am trying to fine tune some code from a Kaggle kernel. The model uses pretrained VGG16 weights (via 'imagenet') for transfer learning. However, I notice there is no layer freezing of layers as is recommended in a keras blog. One approach would... |
H: Role derivative of sigmoid function in neural networks
I try to understand role of derivative of sigmoid function in neural networks.
First I plot sigmoid function, and derivative of all points from definition using python. What is the role of this derivative exactly?
import numpy as np
import matplotlib.pyplot... |
H: What data treatment/transformation should be applied if there are a lot of outliers and features lack normal distribution?
I am solving for a regression use case using tensorflow's DNNRegressor. For EDA purpose, I referred to this post and used pandas boxplot to plot my numerical predictors and target variable(here... |
H: DBSCAN - Space complexity of O(n)?
According to Wikipedia, "the distance matrix of size $\frac{(n^2-n)}{2}$ can be materialized to avoid distance recomputations, but this needs $O(n^2)$ memory, whereas a non-matrix based implementation of DBSCAN only needs $O(n)$ memory."
$\frac{(n^2-n)}{2}$ is basically the triang... |
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