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See the forest

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[Ubuntu] Installation useful program for Ubuntu 22.04 LTS 1. Breaktimer sudo snap install breaktimer 2. Sticky Notes sudo apt-add-repository ppa:umang/indicator-stickynotes sudo apt update sudo apt install indicator-stickynotes 3. Pycharm sudo snap install pycharm-community --classic 4. Jupyter Notebook sudo apt update && upgrade sudo apt install python3 sudo apt install python3-pip sudo pip install jupyter # jupyter --version Connect Jupyter Notebook .. 공감수 0 댓글수 1 2023. 1. 22.
[Ubuntu] Environment setting for Ubuntu 22.04 LTS 1. Korean Language Setting Source : https://shanepark.tistory.com/231 2. Activate root administrator sudo passwd root 3. Update and upgrade packages sudo apt-get update sudo apt-get upgrade -y 4. Install vim sudo apt-get upgrade && sudo apt-get install vim 5. Alter Apt repository mirror Change the Apt repository mirror to a Korean Kakao mirror server to speed up. Open the setting file with follo.. 공감수 0 댓글수 0 2023. 1. 10.
[R] Non-Linear Support Vector Machine 1. What is Non-Linear SVMs? In practice, we can be faced with non-linear class boundaries. We need to consider enlarging the feature space using functions of the predictors such as quadratic and cubic terms, in order to address this non-linearity. In the case of the support vector classifier, we can also enlarge the feature space using quadratic, cubic, and even higher-order polynomial functions.. 공감수 0 댓글수 0 2022. 12. 6.
[R] Support Vector Machine 1. What is Support Vector Machine? Find a plane that separate the classes in feature space. Soften what we mean by separates and enrich and enlarge the feature space so that separation is possible. Three methods for SVM Maximum Margin Classifier Support Vector Classifier(SVC) Support Vector Machine(SVM) 1.1 Hyperplane A hyperplane in p dimensions is a flat affine subspace of dimension p-1. Gener.. 공감수 0 댓글수 0 2022. 12. 6.
[R] Tree-Based Methods : Bayesian Additive Tree 1. What is Bayesian Additive Tree? Bayesian additive regression trees(BART) is another ensemble method that uses decision trees as its building blocks. BART methods combines other ensemble methods : Each tree is constructed in a random manner as in bagging and random forest. Each tree tries to capture signals not yet accounted for by the current model as in boosting. BART method can be viewed as.. 공감수 0 댓글수 0 2022. 11. 27.
[R] Tree-Based Methods : Boosting 1. What is Boosting? Boosting is a general approach that can be applied to many statistical learning methods for regression or classification. Boosting grow trees sequentially. The boosting approach learns slowly. Given the current model, we fit a decision tree to the residuals from the model. We then add this new decision tree into the fitted function in order to update the residuals. Each of t.. 공감수 0 댓글수 0 2022. 11. 27.
[R] Tree-Based Methods : Random Forest 1. What is Random Forest? Random forests provide an improvement over bagged trees by way of a small tweak that decorrelates the trees. This reduces the variance when we average the trees. When building these decision trees, each time a split in a trees is considered, a random selection of m predictors is chosen as split candidates from the full set of p predictors. A fresh selection of m predict.. 공감수 0 댓글수 0 2022. 11. 27.
[R] Tree-Based Methods : Bagging 1. Ensemble methods An ensemble methods is an approach that combines many simple building ensemble block models in order to obtain a single and potentially very powerful model. These simple building block models are sometimes known as weak learners. Methods Bagging Random Forest Boosting Bayesian additive regression trees 2. Bagging 2.1 Boostrap methods Referring (\(X_1, ..., X_n\)) as populatio.. 공감수 0 댓글수 0 2022. 11. 27.
[R] Tree-Based Methods : Advantages and Disadvantages of Tree Advantages Trees are very easy to explain to people. Decision trees more closely mirror human decision-making than do the regression and other classification approaches. Trees can be displayed graphically, and are easily interpreted. Trees can easily handle qualitative predictors without the need to create dummy variables. Disadvantages Trees do not have the same level of predictive accuracy as .. 공감수 0 댓글수 0 2022. 11. 27.
[R] Tree-Based Methods : Classification Decision Tree 1. What is Classification Decision Tree? Predict a qualitative response rather than a quantitative one. Predict that each observation belongs to the most commonly occuring class. Use recursive binary splitting to grow a classification tree. Use classification error rate(missclassification rate) as evaluation metrics. Splitting metrics The classification error rate : \(Error = 1 - max_{k}(\hat{p}.. 공감수 0 댓글수 1 2022. 11. 27.
[R] Tree-Based Methods : Regression Decision Tree 1. Regression Decision Tree 1.1 [Ex] Finding optimal value \(\alpha\) using CV # Import library and dataset library(tree) data(Hitters) # Training models miss 공감수 0 댓글수 0 2022. 11. 27.
[R] Tree-Based Methods : Decision Tree 1. Tree-Based Methods Tree-based methods for regression and classification involve stratifying or segmenting the predictor space into a number of simple regions The set of splitting rules used to segment the predictor space can be summarized in a tree. Tree-based methods are simple and useful for interpretation. Bagging and Random Forests grow multiple trees which are combined to yield a single .. 공감수 0 댓글수 0 2022. 11. 27.
[R] Non-Linear Models : Local Regression, GAM 1. What is Local Regression Local regression or Local polynomial regression, also known as moving regression, is a generalization of the moving average and polynomial regression. Local regression computes the fit at target upoint \(x_0\) using only the regression nearby training obervation. With a sliding weight function, we fit separate linear fits over the range of \(x\) by weighted least squa.. 공감수 0 댓글수 0 2022. 11. 14.
[R] Non-Linear Models : Splines 1. What is Splines? Regression splines are more flexible than polynomials and step functions, and are actually an extension of the two. The divide the range of X into K distinct regions. For each region, a polynomial function is fit to the data, however, the polynomials are constrained so that they join smoothly at the region boundaries or knots. For regression splines, instead of fitting a high.. 공감수 0 댓글수 0 2022. 11. 14.
[R] Non-Linear Models : Step Functions 1. What is Step functions? Polynomial regression extends the linear model by adding extra predictors, obtained by raising each of the original predictors to a power. Cubic regression uses three variables $X_1$, $X_2$, $X_3$ as predictors. This is a simple way to provide a non-linear fit to the data. Step functions cut the range of a variable into K distinct regions in order to produce a qualitat.. 공감수 0 댓글수 0 2022. 11. 14.
[R] Non-Linear Models : Polynomials 1. What is Non-Linear Models Kinds of non-linear models : Polynomials Step functions Splines Local regression Generalized Additive Models(GAM) Population : \(y_i = \sum_{j=1}^p f_j(x_j) + \epsilon_i\) Inference : \(\hat{y}_i = \sum_{j=1}^p \hat{f}_j(x_j)\) 2. Polynomial Regression Not really intersted in the coefficients. More interested in fitted function values. \(\hat{f}(x_0) = \hat{\beta_0} .. 공감수 0 댓글수 0 2022. 11. 14.
[multiprocessing] What is Parallel Processing? 1. What is Parallel Processing In order to process a large dataset in a situation where available memory was limited, data had to be processed by splitting it into chunks. Parallel processing increases the speed of work by simultaneously processing chunks. A central processing unit(CPU) is hardware that processes comuputer operations. The old CPU was able to perform only one task in a single cor.. 공감수 0 댓글수 0 2022. 11. 8.
[R] Classification Problem : QDA, Naive Bayes, KNN 1. QDA(Quardratic Discriminant Analysis) QDA assumes that each class has its own covariance matrix, \(X ~ N(\mu_k, \sum _ k)\) LDA vs QDA Probability : \(P(y_i=k|x)\) X : \(N(\mu_k,\sum)\) vs \(N(\mu_k, \sum_k)\) Parameters : \(\mu_1, ..., \mu_k$ vs $\mu_1, ..., \mu_k, \sum_1, ..., \sum_k\) Num of grids : \(PK + \frac{P(P+1)}{2}$ vs $PK + K\frac{P(P+1)}{2}\) 1.1 [Ex] LDA vs QDA Classification Er.. 공감수 0 댓글수 0 2022. 11. 6.
[R] Assessment of the Performance of Classifier 1. Two types of Missclassification errors We can change the two error rates by changing the thershold from 0.5 to some other value in [0, 1]. \(\hat{Pr}(Default = Yes|Balance, Student) \ge \alpha\) \(\alpha\) is a threshold. If \(\alpha\) ↑, FN increase while FP decrease. If \(\alpha\) ↓, FN decrease while FP increase. 2. [Ex] Changes in errors along with Thresholds thresholds 공감수 0 댓글수 0 2022. 11. 5.
[R] Classification Problem : LDA(Linear Discriminant Analysis) 1. Discriminant Analysis Bayes Theorem : \(Pr(Y=k|X=x) = \frac{\pi_k f_k(x)}{\sum_{l=1}^K \pi_l f_l(x)}\) The density for \(X\) in class \(k\) : \(f_k(x) = \frac{1}{\sqrt{2\pi}\sigma_k}e^{-\frac{(x-\mu_k)^2}{2\sigma_k^2}}\) The prior probability for class \(k\) : \(\pi_k = Pr(Y=k)\) If the distribution of the \(X\) is approximately normal, LDA and QDA is more stable. 1.1 LDA(Linear Discriminant .. 공감수 0 댓글수 0 2022. 10. 31.
[kill process] How to find the Process ID and Kill it Step 1 : Find the Process ID(PID) of the program There are several ways we can use for finding the PID of a process. # If we know the name of the process pidof # Returns all the running process on the system ps aux | grep -i "name of our desired program" ps aux command returns all the running process on the system. And the grep afterwards shows the line which matches with the program name. ps au.. 공감수 0 댓글수 0 2022. 10. 31.
[src] How do i display local image in markdown? Way 1 : Display local image using parenthesis Create a directory named like 'dirs' and put all the images that will be rendered by the Markdown. For example, put image.png into dirs. To load image.png that was located under the dirs directory before. Note that dirs directory must be located under the same directory of our markdown text file which has .md extension. ![image](dirs/image.png) Way 2.. 공감수 0 댓글수 0 2022. 10. 29.
[Q&A] ELT vs ETL Question : ELT vs ETL, Terminologly wise, one does the load before the transformation and one does it after.. But this doesn't make much sense to me and why it's so important? Answer : It's important for cost and future proofing reasons. The reasons we had ETL before was because storage particulary cloud storage was expensive so we had to limit the data we were writing in our warehouse to keep c.. 공감수 0 댓글수 0 2022. 10. 26.
[div] Colored note boxes inside Jupyter Notebooks NOTE Use blue boxes for Tips and notes. Use green boxes sparingly, and only for some specific purpose that the other boxes can't cover. For example, if you have a lot of related content to link to, maybe you decide to use green boxes for related links from each section of a notebook. Use yellow boxes for examples that are not inside code cells, or use for mathematical formulas if needed. In gene.. 공감수 0 댓글수 0 2022. 10. 25.
[Jupyter] Convert ipynb into pdf Step1 : Open the ipynb extension file Open the ipynb file we want to convert to pdf after running Jupyter. Step2 : Download to html Download to html by clicking file - Download as HTML in the upper left corner. Step3 : Convert to pdf at sejda Source from : https://www.sejda.com/ 공감수 0 댓글수 0 2022. 10. 25.
[gitignore] How to ignore files and directory in repository Step 1 : Create .gitignore To create a .gitignore file, go to the root of our local Git, and create it : $ touch .gitignore Step 2 : Append extensions or directory name to .gitignore Now open the file using a text editor. We are just going to add using wild expression. # ignore ALL .log files *.log # ignore All directory named ipynb_checkpoints .ipynb_checkpoints */.ipynb_checkpoints/* Step 3 : .. 공감수 0 댓글수 0 2022. 10. 24.
[Error] 5 Common Python Mistakes and How to Fix Them Mistake1 : Indentation and Spaces The Indentation Error can occur when the spaces or tabs are not placed properly. If an indentation error occurs, the transition options of the tabs and spaces on the editor must be changed. Then tabs will automatically be cnaged to 4 spaces. nums = [11, 30, 44, 54] for num in nums: # Using tabs square = num ** 2 # using spaces(4) print(square) # Indentation Eror.. 공감수 0 댓글수 0 2022. 10. 24.
[venv] How to use Virtual Environment with Built-in venv module Step1 : Creating and activating a new virtual environment $ python3 -m venv project_env $ source project_env/bin/activate We can create virtual environment with system-packages(global installation of Python packages). $ python3 -m venv project_env --system-site-package Step2 : Check installed packages on virtual environment $ pip install pytz $ pip list Package Version ------- ------- certifi 20.. 공감수 0 댓글수 0 2022. 10. 24.
[Dictionary] Big-O Efficiency of Python Dictionary 1. Big-O Efficiency of Python Dictionary Operators One important side note on dictionary performance is that the efficiencies are for average performance. Operation Big-O Efficiency copy O(n) get item O(1) set item O(1) delete item O(1) contains (in) O(1) iteration O(n) 2. Comparison of time efficiency of in operator between list and dictionary We will compare the performance of the contains ope.. 공감수 1 댓글수 0 2022. 10. 21.
[List] Big-O Efficiency of Python List 1. Big-O Efficiency of Python List Operators Operation Big-O Efficiency index [] O(1) index assignment O(1) append O(1) pop() O(1) pop(i) O(n) insert(i, item) O(n) del operator O(n) iteration O(n) contains (in) O(n) get slice [x:y] O(k) del slice O(n) set slice O(n+k) reverse O(n) concatenate O(k) sort O(nlogn) multiply O(nk) 2. Comparison of time efficiency of list.pop() method What we would ex.. 공감수 2 댓글수 0 2022. 10. 20.

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