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H: How does the seed value work in Weka for clustering?
I have recently been learning about the various clustering methods, and I decided to apply Furthest Point clustering in Weka with varying seed values.
The seed value dictates the initial choice of points in the data set. It can't be totally random, since repeate... |
H: What is training
I'm a newbie on deep learning and I have a simple question.
I'm reading some article about the neural network. It says that people created a simple neural network named perceptron. And this network can't solve a very simple question: exclusive or.
I think it's because I don't know how to train a ne... |
H: How to get rid of the expectation in Monte Carlo Policy Gradient method?
Taken from Policy Gradient lecture notes, slide 16 onward:
Here in the David Silver Lecture series I perfectly understand how he took the expectation value in the theorem part by combining the following equation .
But how he removed the exp... |
H: Why I didn't get any significant variable in my logistic model?
I decided to apply the logistic regression method to my categorical and quantitative data.
So, I followed these steps:
Eliminating the bad and inconsistent data.
Preparing the target variable (categorical variable).
Testing the dependencies between ca... |
H: Why are policy gradient methods preferred over value function approximation in continuous action domains?
In value-function approximation, in particular, in deep Q-learning, I understand that we first predict the Q values for each action. However, when there are many actions, this task is not easy.
But in policy i... |
H: Classifier Threshold
I am designing a classifier for an Imbalanced Data set. I have a queries regarding choosing the threshold for a classifier, currently I am using mean of the predicted probabilities as the threshold and I am maximizing the recall on positive classes. Is this a correct way of choosing a threshold... |
H: What is the difference between float64 and double in TensorFlow?
In storing floating point values both overflow and underflow problems cause loss of data. In machine learning tasks underflow is a common problem. I wanted to know if double is better than float64 in TensorFlow or not and if there is any difference be... |
H: How to add incorporate meta data into text classification?
I have a collection of statements which I need to classify into 5 classes. Each statement have meta data in different columns:
Author|Editor| date of release| statement | Class
How can one use the meta data to improve the text classification task?
AI: Some ... |
H: Need a Work-around for OneHotEncoder Issue in SKLearn Preprocessing
So, it seems that OneHotEncoder won't work with the np.int64 datatype (only np.int32)! Here's a sample of code:
import numpy as np
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
a = np.array([[56748683,8511896545,51001984320],... |
H: concatenating the content of list in python
I have a list.
list = ['It is a delightfully naive and entertaining movie',
'The songs are boring and dated in 2009',
'was a great movie from genre director Luc Besson']
and I want a result like:
list_result = [ It is a delightfully naive and entertaini... |
H: How does neural network solve XOR problem
I'm reading a wonderful tutorial about neural network. This is the best tutorial I've ever seen but I can't understand one thing as below:
In the link above, it is talking about how the neural work solves the XOR problem.
It says that we need two lines to separate the four ... |
H: What is Compatible Function Approximation theorem in reinforcement learning?
I am following David Silver's RL course. In the policy gradient section, I found this slide that I would like have an explanation of.
What are these two conditions?
What is the logic behind the first derivative equality? Is it just that... |
H: Regression and Neural networks
I'm trying to restore this function:
$$ F(x) = x*sin(\alpha x)+b; \space\space \alpha,b \in (-20,20) $$
My NN model(with Keras) is:
1 layer: GRU, 9 neurons, selu activation
2 layer: GRU, 3 neurons, selu activation
1 layer: GRU, 7 neurons, selu activation
1 layer: Dense, 1 neuron, li... |
H: Classes of neural nets and their applications
Would you say, you could design, tune and/or train any DNN for any application, or do their designs inherently postulate some specialization?
Is there such a review?
For example, are CNNs better for the spatial domain (e.g image analysis), and RNN for temporal (e.g. ti... |
H: Can linear regression or any other models be used to judge if Y increases as X increases?
I have a database like:
Site X Y
S1 1 1.5
S1 1 1.3
S1 2 1.7
S1 1 1.1
S1 4 5.9
S2 3 4.0
S2 2 2.5
S2 4 9.1
S2 4 9.2
S2 1 2.3
I need to find i... |
H: What is the name for this chart which splits a quantity by allocation to a class hierarchy?
I need to create this type of chart, where you can see a quantity is split into a hierarchy of classes or taxonomy. In the example below the quantity is a household monthly income, and the classes are different monthly expen... |
H: How should classification be done for a very small data set?
I am looking at data from the London Data Store based on social characteristics between London boroughs.
Since there are only about 30 London boroughs, the data sets I am looking at are naturally very small. For example, I might be fitting regression/cor... |
H: First layer weights for transfer learning with new input tensor in keras.applications models?
In the pre-implemented models in keras (VGG16 ect) it is specified that we can change shape of the inputs of the models and still load the pre-trained imagenet weights.
What I am confused about is then what happens to the... |
H: What is the dimensionality of the bias term in neural networks?
I am trying to build a neural network (3 layers, 1 hidden) in Python on the classic Titanic dataset.
I want to include a bias term following Siraj's examples, and the 3Blue1Brown tutorials to update the bias by backpropagation, but I know my dimensiona... |
H: Better input for Doc2Vec
I want to perform Doc2Vec on a twitter dataset. As each tweet consists of a nummber of special characters ,numbers, urls, mentions and hashtags, non-english words, what should be my input for Doc2Vec? How should i approach for initial tweet pre-processing?
I saw many tutorials but all of th... |
H: Cannot see what the "notation abuse" is, mentioned by author of book
From Sutton and Barto, Reinforcement Learning: An Introduction (second edition draft), in equation 3.4 of page 38.
The probabilities given by the four-argument function p completely characterize the dynamics of a
finite MDP. From it, one can co... |
H: What is the best way to normalize histogram vectors to get distribution?
l have the following sample of 4 vectors of dimension 5 . They are sparse vectors, in a way that each value in a vector represent the frequency (number of occurrence of values). For instance
v_1=[0,4,0,0,1]
4 at index 1 means we have four val... |
H: scikit-learn classifier reset in loop
I'm trying to evaluate classifiers comparison by running the sample script that can be found here.
What I noticed is that in some cases the classifier is not reset.
In fact, duplicating some of those (with no parameter change) the score and the countour change between the two.
... |
H: When visualizing data that has <1 or <5 ppm how do you display this?
I have some data (parts per million) where some of it is 1 or greater than one (but has an actual number.) However, some of the data simply lists "<1" ppm.
What is a good way to visualize this in a graph?
Should I pick an arbitrary decimal less th... |
H: When do we say that the dataset is not classifiable?
I have many times analysed a dataset on which I could not really do any sort of classification. To see whether I can get a classifier I have usually used the following steps:
Generate box plots of label against numerical values.
Reduce the dimensionality to 2 or... |
H: What exactly is the input of decoder in autoencoder setup
I am reviewing various autoencoder setups for MNIST reconstruction, Seq2Seq translation and others.
My naive understanding of data flow is as follows:
Input -> [Encoder] -> Hidden Representation -> [Decoder] -> Output.
However, in case of Seq2Seq translati... |
H: Using machine learning technique to predict commodity prices
Has anyone here tried to predict a commodity's price by using other commodities prices as features in a machine learning algorithm? What techniques have been successful?
AI: Based on your question there are couple of things which I would assume to answer ... |
H: How word embedding work for word similarity?
I am trying to apply word2vec/doc2vec to find similar sentences. First consider word2vec for word similarity. What I understand is, CBOW can be used to find most suitable word given a context, whereas Skip-gram is used to find the context given some word, so in both case... |
H: What is the definition of Data Scout?
I am looking for a definition of Data Scout.
what is the difference between Data Mining and Data Scout?
AI: In business, there is little time to look through the data that has been eliminated before a specific analysis project so I put that data in an elim_bin (project code#, p... |
H: Are neural networks able to deal with non-normalised inputs?
All the techniques/models that I have learnt so far for deep learning start with some sort of normalization to the features, for example gaussian method, minmax scaling, robust scaling, batch normalization, instance normalization.
Are there any techniques... |
H: How to find a similarity value between cars
So I have a database of web-scraped cars, and I want to find the similarity between cars based on the km driven (e.g 69000), the model year (e.g 2012), and the trim of the car which will be one of three categories: base, mid, top.
What similarity measure can I use that wi... |
H: Neural Networks overfitting
How the neural networks are overfitted for regression.Either it tries
to equal individual observation values or equals to the sum of all observations
AI: Neural Networks basically act as a high memory-based machine learning algorithm. So for a given dataset the chance of it perfectly ali... |
H: What are the best ways to use a time series data for binary classification
I have large number of csv files and each of them are timeseries based csv files sampled at Avery 5 seconds for 2-3 mins. I have 20k such files with 200-300 variables in each file.
I am aggregating the data by mean over the entire 2-3 mins w... |
H: Understanding what is going on
I have a collection of 1000s of bottles of wine. I want to understand what could / likely to be driving the price either up or down. Below is an example of the data
Wine Country Area Grape Class Year Price
A France Burgundy Pinot Noir ... |
H: Softmax classifier never allows for 100% probability in LSTM?
When working with LSTM I am using a softmax classifier and a one-hot encoded vector approach. The softmax looks like this:
$$S(h_i) = \frac{e^{h_i}}{\sum e^{h_{total}}}$$
notice, LSTM's result is a $h=tanh(c) \circ \sigma(p)$
Where c is the cell state pa... |
H: Implementing a CNN with one convolution layer
I am trying to implement a 1 channel CNN by slightly changing this article: this article.
The problem is that I am new to keras and deep learning and I don't know this far why I am getting this error:
ValueError: Negative dimension size caused by subtracting 100 from 1 ... |
H: Exploration vs exploitation tradeoff to find a price that maximizes revenue
Is there a practical strategy that can learn to price a product optimally? Right now I have the following arbitrary hill-climbing algorithm:
Run an experiment at starting price P and gather 500 data points (e.x. 20 buy and 480 not buy).
R... |
H: What is the difference between CountVectorizer token counts and TfidfTransformer with use_idf set to False?
We can use CountVectorizer to count the number of times a word occurs in a corpus:
# Tokenizing text
from sklearn.feature_extraction.text import CountVectorizer
count_vect = CountVectorizer()
X_train_counts =... |
H: My LSTM can't reduce error down to zero, when overfitting
I have implemented LSTM in c++ which steadily decreases in error, but slows down at the certain error value. It also seems to predict most of the characters, but gets stuck and not able to correct some mistakes (or will correct them very slowly), even after ... |
H: After choosing top models in classification? Can I apply it on the rest of my dataset
I am working with a corpus that has 5 datasets in product reviews (A, B, C, D and E), mine is a text classification problem and I need to find the best 5 top models in terms of classification performance (F1).
I started with colle... |
H: Interpret clustering results after variable transformation
since some time I have a question to which I have not found the proper answer yet.
My doubt concerns the interpretation of the results of a clustering algorithm which was run on features to which a log-transformation was applied.
Specifically, let's assume ... |
H: Building CNN, Need More Images
I'm building a custom Convolutional Neural Network for image recognition. I'm running into the issue of only having around 100 images or so to train and test on. From my research and model results, this is not enough.
Does anyone who of a service for hire to have someone search we... |
H: What is LSTM, BiLSTM and when to use them?
I am very new to Deep learning and I am particularly interested in knowing what are LSTM and BiLSTM and when to use them (major application areas). Why are LSTM and BILSTM more popular than RNN?
Can we use these deep learning architectures in unsupervised problems?
AI: RNN... |
H: Using ML to create unique descriptors?
I have a problem that doesn't seem to fall into a common machine learning category, and I was wondering if this still could potentially be solved with ML.
Problem: I have two signals recorded from two sensors, and would like to determine whether they are correlated (i.e. recor... |
H: AttributeError: 'numpy.ndarray' object has no attribute 'predict'
I have trained and saved a model :
import numpy as np
# load the dataset
dataset = np.loadtxt("modiftrain.csv", delimiter=";")
# split into input (X) and output (Y) variables
X_train = dataset[:,0:5]
Y_train = dataset[:,5]
from sklearn.naive_bayes... |
H: Reinforcement Learning - What's the formula for the value function
I'm trying to implement a value-iteration algorithm to solve a grid-world problem (I'm new to the field). The usual formula that I encounter about the value function V(s) is:
$$V(s) = R(s) + max_{a \in A} \sum_{s' \in S} T(s, a, s') V(s')$$
where $S... |
H: About training and cross validation on a time series problem
I am new to machine learning.
I'm having a task of predicting whether a user will churn in March, given February feature data and the churn result. However, March data is leaked and now I'm assigned to predict for April.
My strategy is to train the mode... |
H: Classify future performance of customer
I have a dataset with monthly revenue per customer. I want to build a model that can try to predict if the customer will exceed $10,000 3 months out (yes/no).
While this seems like a traditional ML problem I have an important questions
Should I build my dataset with one row ... |
H: What exactly is a class in kNN search?
I am trying to create a kNN search from scratch for a project. I think I have the concept of it and how it works, but I can't understand what exactly is a class.
I have a matrix of $X*Y$ where $X$ is the number of elements and $Y$ their vector. So if the values are completely... |
H: Predicting Missing Features
I have "millions" of items each having N binary features. When a feature is "0" it could be that the information is simply missing. So, given the data with the currently observed 1's, I would like to have a probability of the "0" features being "1".
I am thinking this can be a Neural net... |
H: Text annotating process, quality vs quantity?
I have a question regarding annotating text data for classification.
Assume we have ten volunteers who are about to annotate a large number of texts into label A or B. They probably won't have time to go through all the text samples, but at least a significant portion o... |
H: How can autoencoders be used for clustering?
Suppose I have a set of time-domain signals with absolutely no labels. I want to cluster them in 2 or 3 classes. Autoencoders are unsupervised networks that learn to compress the inputs. So given an input $x^{(i)}$, weights $W_1$ and $W_2$, biases $b_1$ and $b_2$, and ou... |
H: What is the difference between "expected return" and "expected reward" in the context of RL?
The value of a state $s$ under a certain policy $\pi$, $V^\pi(s)$, is defined as the "expected return" starting from state $s$. More precisely, it is defined as
$$
V^\pi(s) = \mathbb{E}\left(R_t \mid s_t = s \right)
$$
wher... |
H: Can dropout and batch normalization be applied to convolution layers
Can dropout be applied to convolution layers or just dense layers. If so, should it be used after pooling or before pooling and after applying activation?
Also I want to know whether batch normalization can be used in convolution layers or not.
I'... |
H: Idf values of English words
I'm working on keyword/phrase extraction from a single document. I started by doing term frequency analysis, but this returns words like "new" which aren't very helpful. So I want to penalize the common words and phrases, for which we normally use idf (inverse document frequency). But si... |
H: What it Would be easier Building a Deep Net From Scratch or Using an existing Architecture?
In Practice with CNN what would be easier: Building a CNN from scratch or using a an existing architecture with some updates?
AI: It depends on your task and the amount of data you have. If you have so much data but you can ... |
H: Use 1 or 2 norm for Voronoi vector quantization?
I have a script from a lecture. Basically it says that based on the Voronoi partitioning we identify the corresponding (nearest) class $w_k$ to a vector $x$ where $\left| {{w_k} - x} \right| = \mathop {\min }\limits_i \left( {\left| {{w_i} - x} \right|} \right)$ give... |
H: Updating the weights of the filters in a CNN
I am currently trying to understand the architecture of a CNN. I understand the convolution, the ReLU layer, pooling layer, and fully connected layer. However, I am still confused about the weights.
In a normal neural network, each neuron has its own weight. In the full... |
H: Grouping of similar looking text
I have a data frame which has two columns, "Title" and "Description". The title column has a bunch of titles related to clinical lab tests. Unfortunately, most of the titles are a repeat of the same test but, due to minor changes in the titles, titles are shown as unique.
values = [... |
H: A math question about solving the Lagrangian of Support Vector Machine
$$\mathcal{L}(w,b,\xi,\alpha,r) = \frac12w^Tw+C\sum_{i=1}^m \xi_i-\sum_{i=1}^m \alpha_i[y^{(i)}(x^Tw+b)-1+\xi_i]-\sum_{i=1}^mr_i\xi_i$$
Here, the $\alpha_i$'s and $r_i$'s are our Lagrange multipliers (constrained to be $\ge 0$)
To maximize the L... |
H: Creating dummy variables to match fitted model at inference
I have built a machine learning classifier using Sklearn and pandas as my main tools. Now, one of the input features to the model is country (to letter country code such as US). I have fit a model using the pd.get_dummies function.
Now I want to run infer... |
H: Plot of two different matrices in R
I'm trying to plot two different size matrices using one graph (in R), but can't manage to do so.
I tried using matplot and the regular plot, but it didn't work.
Does anyone know how to plot it?
AI: (Assuming this is a similar question to what was posted on Cross Validated, but w... |
H: How to correctly infer vectors in Gensim doc2vec?
I would like to know which is the correct procedure for inferring vectors in Gensim doc2vec.
I have a dataframe df with a feature, called name, and composed of two subsets train and test.
df = train + test
My aim is to find the most similar name in train given a na... |
H: Incorporate luck in statistical modelling
I was wondering if it is possible (and if yes, how is it done) to incorporate a luck component in statistical models.
So let’s assume I’d perform a regression on the goal difference between two teams in order to model the outcome of a sports match. How could I also model a... |
H: How to persist data scaler for predictions
I have a Support Vector Machine in Scikit-learn (Python) that gets trained once in a while when enough new data has accumulated (user help train the model by submitting new data).
I store the model in .pkl format for persistence. However, the SVM needs scaled data and I'm... |
H: How do I implement the sigmoid function in Octave?
so given that the sigmoid function is defined as hθ(x) = g(θ^(T)x), how can I implement this funcion in Octave given that g = zeros(size(z)) ?
AI: This will compute the sigmoid of a scalar, vector or matrix.
function g = sigmoid(z)
% SIGMOID Compute sigmoid funct... |
H: Layman's comparison of RMSE
I don't have a maths / stats / data science background and need to evaluate which of the two evaluations below (numerical regression on Amazon Machine Learning) predict more accuracy. Both models use the same data set but it's looking at different time frames both on the independent and ... |
H: How to create an array from the list of arrays in python
I was trying to write a python code that can set some neural network channels or neurons to zero at the inference; and I wrote the code below. The code generates 10 different arrays for different percentage of the channels or neurons that are set to zero. My ... |
H: Advice and Ideas appreciated - Machine Learning one man project
I have a project where I am supposed to start from scratch and learn how machine Learning works. So far everything is working out better than expected but I feel as I am offered to many ways to choose from.
My Project:
I have data with 700 rows and 108... |
H: XGBoost validation for number of trees
I have a simple Question:
I am using XGBoost to classify some data:
1.) With 100 estimators I have following scores(roc_score):
train_data : 98.5
validation_data : 97.2
2.) With 500 estimators I have following scores(roc_score):
train_data : 99.4
validation_dat... |
H: Confusion matrix - determine the values of FP FN TP and TN
After running my code ,I get the values of accuracy, precision and recall and I want t determine the values of FP FN TP and TN from these metrics. I tried to calculate it using the formula of each metric but I couldn't. Is there any way to do this?
AI: You ... |
H: Pandas: How can I update dataframe values?
I have two spreadsheets where one is updating the other.
How can I update this data using the pandas library?
Example, where 'b' updates 'a':
a = {'field': ['a', 'b', 'c'], 'value': ["", None, 1]}
b = {'field': ['a', 'b', 'd'], 'value': [1, 2, 1]}
Expected outcome:
c = {'... |
H: feature extraction from single word for classification into nouns and names
I would like to write a NN that can classify different kinds of words(e.g. nouns,verbs,names) and am struggling to find information on how to do feature extraction on single words.For example, i would like the NN to learn that "street" is a... |
H: TensorFlow MLP loss increasing
When I train my model the loss increases over each epoch. I feel like this is a simple solve and I am missing something obvious but I cannot figure out what is it. Any help would be greatly appreciated.
The neural network:
def neural_network(data):
hidden_L1 = {'weights': tf.Varia... |
H: random forest classifier - impact of small n_estimator and repeated training
trying to have a better understanding of random forest algorithm here. With the same training and holdout datasets, I tried two things here:
Set a small n_estimator (10), train on my training dataset and apply to my holdout dataset. If I ... |
H: Substituting nan values with mean code
for x in num_cols:
imp = SimpleImputer(missing_values=np.nan, strategy='mean')
imp.fit(np.array(ds[x]).reshape(-1,1))
ds[x] = imp.transform(np.array(ds[x]).reshape(-1,1))
AI: Here, you are substituting the missing values (nans) with something, it can be either the ... |
H: Similarity of XGBoost models?
Is xgboost with n_estimators = 100 and learning_rate = 0.1, same as xgboost with n_estimators = 50 and learning_rate = 0.2 ?
AI: No, they won't have neither the same performance nor the same architecture if you were to try to visualize it. An XGBoost with 100 n_estimators and a learnin... |
H: Should I have "normal" sampled data in my dataset?
I am busy working on a project to find the reasons why kids in normal households are doing badly in school.
I have a dataset of which consists of kids that live in environments where the family is middle class, has access to necessary facilities and the kid is not ... |
H: Membership ratio graph
Does anyone know how the what the kind of graph in this image is called?
Each color represents a class and the height of the color, in a particular instant, represents the ratio of elements that belong to that class.
How can I produce such a graph, as an example, in R or Python?
AI: This look... |
H: pandas.isna() vs pandas.DataFrame.isna()
I've seen the two documentation pages for pandas.isna() and pandas.DataFrame.isna() but the difference is still unclear to me. Could someone explain the difference to me using examples?
AI: They call the same underlying method, so there is no functional difference.
Calling ... |
H: Can a decision tree learn to solve a xOR problem?
I have read online that decision trees can solve xOR type problems, as shown in images (xOR problem: 1) and (Possible solution as decision tree: 2).
My question is how can a decision tree learn to solve this problem in this scenario. I just don't see a way for any m... |
H: Is it possible to use an array of graph coordinates as an input variable?
Say I have 1000 graphs that shows sales every year for the last 10 years for 1000 different companies. And say each of those graphs belong to either domestic countries or foreign countries.
Is it possible I could input the different graphs i... |
H: Normal equation for linear regression
I am going through the derivation of normal equation for multivariate linear regression. The equation is given by :
$\theta = (X^{T}X)^{-1}X^{T}Y$
The cost function is given by:
$J(\theta) = \frac{1}{2m}(X\theta-Y)^{T}(X\theta-Y)$
Simplifying,
$J(\theta) = \frac{1}{2m}(\theta^... |
H: Error encoding categorical features using sklearn pipelines
I am new to sklearn pipelines and am using this post as a guide for my code:
https://www.codementor.io/bruce3557/beautiful-machine-learning-pipeline-with-scikit-learn-uiqapbxuj
I am trying to encode a categorical features using a transformation pipeline, b... |
H: Rank links from rss feed
I am trying to create a script to filter the most "intersting" articles from an rss feed and rank them.
feeds = ['http://feeds.theguardian.com/theguardian/technology/rss',
'http://rss.cnn.com/rss/money_news_international.rss',
'https://news.ycombinator.com/rss?format=xml',... |
H: Create Nodes/Edges From CSV (latitude and longitude) for Graphs
The Ultimate Goal: I want to find the shortest and coolest (in terms of temperature) path between two points (for a given pair of latitudes and longitudes on the map)!
I am aware of algorithms like Dijkstra or A*, which are apparently the ones are use... |
H: Tensorflow uses more memory, the more epochs it completes
I created a genetic algorithm "optimizer" for Tensorflow but it is written in python. I know TensorFlow was not designed like this and I need to rather create the optimizer in C++ using their API's but I found out about it only after I already programmed the... |
H: Neural Network Multiple | Averging predictions
I am training multiple neural networks with various parameters. I am trying to average their predictions, but I am not really sure what that means, I am confused about what to average exactly. Here is what I mean: For a single observation in binary classification for e... |
H: Risk score from Neural Network classifier (more than 2 categories)
I am trying to use a Neural Network to perform multiclass classification. The classes represent Insurance Risk Level. The most risky level is Level 1, the least risk corresponds to Level 10. The labels came from Unsupervised clustering unannotated i... |
H: Split time series by python or by keras?
In Python you can use TimeSeriessplit() to split a time series properly for training but you can also do the same(?) in Keras by TimeseriesGenerator.
Which one is recommendable? And/or what are certain differences?
AI: Assuming they can both do what you want them for (can pe... |
H: Neural Networks for Unsupervised Learning
Why cannot we use neural networks for unsupervised learning problem.
I do know that it can be used using the Kohenon’s Self Organizing Map (KSOM) but is this the only method that we can use or are there any other.
AI: Yes, there are others. The most important dimensionality... |
H: Why is my prediction using ARIMA better if I'm using less historic data?
I have a data set containing hourly electricity prices for since 1.01.19 until September. Since the process turned out to be (weakly) stationary, I applied an ARIMA model in Python in order to predict the prices for the next day.
It turned ou... |
H: Are validation sets necessary for Random Forest Classifier?
Is it necessary to have train, test and validation sets when using random forest classifier?
I understand it is important with Neural Networks but I am not understanding the importance of it with RF. I understand the idea of having a third unseen set of da... |
H: How to arrange the sets to predict y on x in time series?
I'm performing my first NN with my own data and while I was already tuning the parameters I stumbled over an aspect which confuses me now such that I'm not sure what is right and what is wrong..
Given this (head of the) data df_train1_raw:
... |
H: General approach on time series for customer retention/churn in retail
I have a time series of data in the following form:
| purchase_date | cutomer_id | num_purchases | churned |
2018-10-31 id1 39 0
2018-11-31 id1 0 0
2019-0... |
H: Conceptual questions on MLP and Perceptrons
I am facing some confusion regarding the terminologies assocaiated to classification and regression problems esp. using the MLP and Perceptron models.
These are the following:
1) When the data is linearly inseparable, we use MLP. Here what is meant b "data"--is it the re... |
H: class_weight on sklearn's DecisionTreeClassifier
Can class_weight='balanced' on scikit-learn's DecisionTreeClassifier be interpreted as having identical duplicate data points for the minority classes?
I know that doesn't work that way, class_weight works as a misclassification cost. But I want to understand if it w... |
H: clarification on train, test and val and how to use/implement it
So far I think I understood the differences between the training, test and validation set. Basically it is like in this image:
Training set: The data where the model is trained on
Validation set: Data the model has not been trained on and used to tun... |
H: Convey time lag information to a linear regression model
I am using a simple linear regression to predict the number of units an item has moved and price of the item is one of the input parameters.
For a few items, the older prices are not relevant and hence this results in incorrect predictions. The definition of... |
H: Sublime Text 2 with Pandas for Excel (Combining Data) & Data Visualization
I'm new to programming with Python, and so far it's been a headache to create a build environment- and need your support and expertise in this area.
Background I'm running a MacBook, and using Sublime Text 2, and needing to learn python. I'd... |
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