_id stringlengths 2 6 | text stringlengths 3 395 | title stringclasses 1 value |
|---|---|---|
C5900 | Below are the methods to convert a categorical (string) input to numerical nature:Label Encoder: It is used to transform non-numerical labels to numerical labels (or nominal categorical variables). Convert numeric bins to number: Let's say, bins of a continuous variable are available in the data set (shown below). | |
C5901 | The quality loss function as defined by Taguchi is the loss imparted to the society by the product from the time the product is designed to the time it is shipped to the customer. In fact, he defined quality as the conformity around a target value with a lower standard deviation in the outputs. | |
C5902 | Distance Learning Off-line is a mode of delivery that does not require online participation. You do not have to come to campus. Course materials may be available through the internet, but they can also be mailed to you if you prefer. | |
C5903 | A set of values or elements that is statistically random, but it is derived from a known starting point and is typically repeated over and over. It is called "pseudo" random, because the algorithm can repeat the sequence, and the numbers are thus not entirely random. | |
C5904 | 5 Real-World Problems Big Data Can SolveHelp Overcome Fertility Issues. According to the Centers for Disease Control (CDC) about 10 percent of American women have trouble getting or staying pregnant. Provide Small-Dollar Loans to People in Need. Put Students Out of Their Misery. Read Our Minds. Catch Terrorists. | |
C5905 | (non-RAN-duh-mized KLIH-nih-kul TRY-ul) A clinical trial in which the participants are not assigned by chance to different treatment groups. Participants may choose which group they want to be in, or they may be assigned to the groups by the researchers. | |
C5906 | Time series data is data that is collected at different points in time. This is opposed to cross-sectional data which observes individuals, companies, etc. at a single point in time. Because data points in time series are collected at adjacent time periods there is potential for correlation between observations. | |
C5907 | Exponential Moving Average (EMA) and Simple Moving Average (SMA) are similar in that they each measure trends. More specifically, the exponential moving average gives a higher weighting to recent prices, while the simple moving average assigns equal weighting to all values. | |
C5908 | The Z value for 95% confidence is Z=1.96. [Note: Both the table of Z-scores and the table of t-scores can also be accessed from the "Other Resources" on the right side of the page.] What is the 90% confidence interval for BMI? (Note that Z=1.645 to reflect the 90% confidence level.) | |
C5909 | resample Function One resampling application is the conversion of digitized audio signals from one sample rate to another, such as from 48 kHz (the digital audio tape standard) to 44.1 kHz (the compact disc standard). resample applies a lowpass filter to the input sequence to prevent aliasing during resampling. | |
C5910 | In probability and statistics, the quantile function, associated with a probability distribution of a random variable, specifies the value of the random variable such that the probability of the variable being less than or equal to that value equals the given probability. | |
C5911 | In (and after) TensorFlow version 0.11. 0RC1, you can save and restore your model directly by calling tf. train. export_meta_graph and tf. | |
C5912 | When working with box plots, the IQR is computed by subtracting the first quartile from the third quartile. In a standard normal distribution (with mean 0 and standard deviation 1), the first and third quartiles are located at -0.67448 and +0.67448 respectively. Thus the interquartile range (IQR) is 1.34896. | |
C5913 | The Area Under the Curve (AUC) is the measure of the ability of a classifier to distinguish between classes and is used as a summary of the ROC curve. The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes. | |
C5914 | Top Algorithms/Data Structures/Concepts every computer science student should knowInsertion sort, Selection sort,Merge Sort, Quicksort.Binary Search.Breadth First Search (BFS)Depth First Search (DFS)Lee algorithm | Shortest path in a Maze.Flood fill Algorithm.Floyd's Cycle Detection Algorithm.More items• | |
C5915 | Factor Analysis in SPSS To conduct a Factor Analysis, start from the “Analyze” menu. This dialog allows you to choose a “rotation method” for your factor analysis. This table shows you the actual factors that were extracted. E. Finally, the Rotated Component Matrix shows you the factor loadings for each variable.More items | |
C5916 | The coefficient of determination can also be found with the following formula: R2 = MSS/TSS = (TSS − RSS)/TSS, where MSS is the model sum of squares (also known as ESS, or explained sum of squares), which is the sum of the squares of the prediction from the linear regression minus the mean for that variable; TSS is the | |
C5917 | An overt integrity test is a self-report paper and pencil test that asks a subject directly about their honesty, criminal history, attitudes towards drug use, thefts by other people, and general questions that show integrity. | |
C5918 | As we saw above, KNN algorithm can be used for both classification and regression problems. The KNN algorithm uses 'feature similarity' to predict the values of any new data points. This means that the new point is assigned a value based on how closely it resembles the points in the training set. | |
C5919 | Mean DeviationFind the mean of all values.Find the distance of each value from that mean (subtract the mean from each value, ignore minus signs)Then find the mean of those distances. | |
C5920 | Choosing the right Activation FunctionSigmoid functions and their combinations generally work better in the case of classifiers.Sigmoids and tanh functions are sometimes avoided due to the vanishing gradient problem.ReLU function is a general activation function and is used in most cases these days.More items• | |
C5921 | The Basics of a One-Tailed Test Hypothesis testing is run to determine whether a claim is true or not, given a population parameter. A test that is conducted to show whether the mean of the sample is significantly greater than and significantly less than the mean of a population is considered a two-tailed test. | |
C5922 | 0:003:17Suggested clip · 116 secondsMaximum Likelihood estimation: Poisson distribution - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C5923 | Linear regression attempts to model the relationship between two variables by fitting a linear equation (= a straight line) to the observed data. One variable is considered to be an explanatory variable (e.g. your income), and the other is considered to be a dependent variable (e.g. your expenses). | |
C5924 | SOLUTION: sample siae =400; sample mean = 44; sample standard deviation =16. what is the margin of error? I have: 44-400/16=356/16=22.30 E=2.16/400=32/400=. 2E-4 margin or error. | |
C5925 | The residual plot shows a fairly random pattern - the first residual is positive, the next two are negative, the fourth is positive, and the last residual is negative. This random pattern indicates that a linear model provides a decent fit to the data. | |
C5926 | The hazard function is not a density or a probability. However, we can think of it as the probability of failure in an infinitesimally small time period between y and y + ∂y given that the subject has survived up till time y. | |
C5927 | The joint probability is symmetrical, meaning that P(A and B) is the same as P(B and A). The calculation using the conditional probability is also symmetrical, for example: P(A and B) = P(A given B) | |
C5928 | 11 websites to find free, interesting datasetsFiveThirtyEight. BuzzFeed News. Kaggle. Socrata. Awesome-Public-Datasets on Github. Google Public Datasets. UCI Machine Learning Repository. Data.gov.More items | |
C5929 | Example of Law of Large Numbers Let's say you rolled the dice three times and the outcomes were 6, 6, 3. The average of the results is 5. According to the law of the large numbers, if we roll the dice a large number of times, the average result will be closer to the expected value of 3.5. | |
C5930 | 2:4518:31Suggested clip · 116 secondsCorrespondence Analysis using SPSS - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C5931 | That is, a studentized residual is just a deleted residual divided by its estimated standard deviation (first formula). In general, studentized residuals are going to be more effective for detecting outlying Y observations than standardized residuals. | |
C5932 | Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply. | |
C5933 | SYNONYMS. idea, notion, conception, abstraction, conceptualization. theory, hypothesis, postulation. belief, conviction, opinion, view, image, impression, picture. | |
C5934 | The main difference between cluster sampling and stratified sampling is that in cluster sampling the cluster is treated as the sampling unit so sampling is done on a population of clusters (at least in the first stage). In stratified sampling, the sampling is done on elements within each stratum. | |
C5935 | A statistic d is called an unbiased estimator for a function of the parameter g(θ) provided that for every choice of θ, Eθd(X) = g(θ). Any estimator that not unbiased is called biased. The bias is the difference bd(θ) = Eθd(X) − g(θ). We can assess the quality of an estimator by computing its mean square error. | |
C5936 | Sigmoid. Sigmoid takes a real value as input and outputs another value between 0 and 1. It's easy to work with and has all the nice properties of activation functions: it's non-linear, continuously differentiable, monotonic, and has a fixed output range. It is nonlinear in nature. | |
C5937 | In many applications including econometrics and biostatistics a fixed effects model refers to a regression model in which the group means are fixed (non-random) as opposed to a random effects model in which the group means are a random sample from a population. | |
C5938 | Common LDA limitations: Fixed K (the number of topics is fixed and must be known ahead of time) Uncorrelated topics (Dirichlet topic distribution cannot capture correlations) Non-hierarchical (in data-limited regimes hierarchical models allow sharing of data) | |
C5939 | The geometric mean differs from the arithmetic average, or arithmetic mean, in how it is calculated because it takes into account the compounding that occurs from period to period. Because of this, investors usually consider the geometric mean a more accurate measure of returns than the arithmetic mean. | |
C5940 | In order to measure the difference between two colors, the difference is assigned to a distance within the color space. | |
C5941 | The method of least squares is about estimating parameters by minimizing the squared discrepancies between observed data, on the one hand, and their expected values on the other (see Optimization Methods). | |
C5942 | The main difference between quota and stratified sampling can be explained in a way that in quota sampling researchers use non-random sampling methods to gather data from one stratum until the required quota fixed by the researcher is fulfilled. | |
C5943 | K-Means clustering algorithm fails to give good results when the data contains outliers, the density spread of data points across the data space is different and the data points follow non-convex shapes. | |
C5944 | Poisson approximation to the Binomial The approximation works very well for n values as low as n = 100, and p values as high as 0.02. | |
C5945 | Probability sampling gives you the best chance to create a sample that is truly representative of the population. Using probability sampling for finding sample sizes means that you can employ statistical techniques like confidence intervals and margins of error to validate your results. | |
C5946 | We can calculate the mean and standard deviation of a given sample, then calculate the cut-off for identifying outliers as more than 3 standard deviations from the mean. We can then identify outliers as those examples that fall outside of the defined lower and upper limits. | |
C5947 | Apart from overfitting, Decision Trees also suffer from following disadvantages: 1. Tree structure prone to sampling – While Decision Trees are generally robust to outliers, due to their tendency to overfit, they are prone to sampling errors. | |
C5948 | 1. If having conditional independence will highly negative affect classification, you'll want to choose K-NN over Naive Bayes. Naive Bayes can suffer from the zero probability problem; when a particular attribute's conditional probability equals zero, Naive Bayes will completely fail to produce a valid prediction. | |
C5949 | from keras. datasets import mnist. from keras. models import Sequential. from keras. from keras. utils import np_utils. # load data. (X_train, y_train), (X_test, y_test) = mnist. load_data()# flatten 28*28 images to a 784 vector for each image. num_pixels = X_train. shape[1] * X_train. shape[2] X_train = X_train. | |
C5950 | A Dataset is a strongly typed collection of domain-specific objects that can be transformed in parallel using functional or relational operations. Each Dataset also has an untyped view called a DataFrame , which is a Dataset of Row . Operations available on Datasets are divided into transformations and actions. | |
C5951 | Grid-searching is the process of scanning the data to configure optimal parameters for a given model. Depending on the type of model utilized, certain parameters are necessary. Grid-searching can be applied across machine learning to calculate the best parameters to use for any given model. | |
C5952 | Feature identification is a well-known technique to identify subsets of a program source code activated when exercising a functionality. We present an approach to feature identification and comparison for large object-oriented multi-threaded programs using both static and dynamic data. | |
C5953 | In a true experiment, participants are randomly assigned to either the treatment or the control group, whereas they are not assigned randomly in a quasi-experiment. Thus, the researcher must try to statistically control for as many of these differences as possible. | |
C5954 | Unlike the batch gradient descent which computes the gradient using the whole dataset, because the SGD, also known as incremental gradient descent, tries to find minimums or maximums by iteration from a single randomly picked training example, the error is typically noisier than in gradient descent. | |
C5955 | The Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images. It was developed by John F. Canny in 1986. Canny also produced a computational theory of edge detection explaining why the technique works. | |
C5956 | Downside deviation measures to what extent an investment falls short of your minimum acceptable return by calculating the negative differences from the MAR, squaring the sums, and dividing by the number of periods, and taking the square root. | |
C5957 | Inductive Learning is a powerful strategy for helping students deepen their understanding of content and develop their inference and evidence-gathering skills. In an Inductive Learning lesson, students examine, group, and label specific "bits" of information to find patterns. | |
C5958 | T - test is used to if the means of two populations are equal (assuming similar variance) whereas F-test is used to test if the variances of two populations are equal. F - test can also be extended to check whether the means of three or more groups are different or not (ANOVA F-test). | |
C5959 | Classification and regression tree (CART) analysis recursively partitions observations in a matched data set, consisting of a categorical (for classification trees) or continuous (for regression trees) dependent (response) variable and one or more independent (explanatory) variables, into progressively smaller groups ( | |
C5960 | Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model. | |
C5961 | TensorBoard is a suite of web applications for inspecting and understanding your TensorFlow runs and graphs. TensorBoard currently supports five visualizations: scalars, images, audio, histograms, and graphs. | |
C5962 | Qualitative data and quantitative data There are two types of data in statistics: qualitative and quantitative. | |
C5963 | A generative adversarial network (GAN) is a machine learning (ML) model in which two neural networks compete with each other to become more accurate in their predictions. GANs typically run unsupervised and use a cooperative zero-sum game framework to learn. Essentially, GANs create their own training data. | |
C5964 | The correlation coefficient is determined by dividing the covariance by the product of the two variables' standard deviations. Standard deviation is a measure of the dispersion of data from its average. | |
C5965 | 1 Answer. They are essentially the same; usually, we use the term log loss for binary classification problems, and the more general cross-entropy (loss) for the general case of multi-class classification, but even this distinction is not consistent, and you'll often find the terms used interchangeably as synonyms. | |
C5966 | The chi-square distribution curve is skewed to the right, and its shape depends on the degrees of freedom df. For df > 90, the curve approximates the normal distribution. Test statistics based on the chi-square distribution are always greater than or equal to zero. | |
C5967 | SGD randomly picks one data point from the whole data set at each iteration to reduce the computations enormously. It is also common to sample a small number of data points instead of just one point at each step and that is called “mini-batch” gradient descent. | |
C5968 | Computer Vision. Image processing is mainly focused on processing the raw input images to enhance them or preparing them to do other tasks. Computer vision is focused on extracting information from the input images or videos to have a proper understanding of them to predict the visual input like human brain. | |
C5969 | Supervised clustering is applied on classified examples with the objective of identifying clusters that have high probability density to a single class. Semi-supervised clustering is to enhance a clustering algorithm by using side information in clustering process. | |
C5970 | The following seven techniques can help you, to train a classifier to detect the abnormal class.Use the right evaluation metrics. Resample the training set. Use K-fold Cross-Validation in the right way. Ensemble different resampled datasets. Resample with different ratios. Cluster the abundant class. Design your own models. | |
C5971 | 1 : a branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data. 2 : a collection of quantitative data. | |
C5972 | Face validity: Does the content of the test appear to be suitable to its aims? Criterion validity: Do the results correspond to a different test of the same thing? | |
C5973 | A time series is a stochastic process that operates in continuous state space and discrete time set. A stochastic process is nothing but a set of random variables. It is a time dependent random phenomenon. Same is time series. | |
C5974 | Basically it is a way to describe important visual features in such a way that they are found again even if the size and orientation of them changes in the future. | |
C5975 | Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. Decision trees are among the most popular machine learning algorithms given their intelligibility and simplicity. | |
C5976 | A discrete variable is a variable whose value is obtained by counting. A continuous variable is a variable whose value is obtained by measuring. A discrete random variable X has a countable number of possible values. | |
C5977 | Page 1. RANDOM VARIABLES. Random Processes: A random process may be thought of as a process where the outcome is probabilistic (also called stochastic) rather than deterministic in nature; that is, where there is uncertainty as to the result. Examples: 1. Tossing a die – we don't know in advance what number will come | |
C5978 | Regression coefficients represent the mean change in the response variable for one unit of change in the predictor variable while holding other predictors in the model constant. The coefficient indicates that for every additional meter in height you can expect weight to increase by an average of 106.5 kilograms. | |
C5979 | Artificial Intelligence enhances the speed, precision and effectiveness of human efforts. In financial institutions, AI techniques can be used to identify which transactions are likely to be fraudulent, adopt fast and accurate credit scoring, as well as automate manually intense data management tasks. | |
C5980 | Relationship extraction is the task of extracting semantic relationships from a text. Extracted relationships usually occur between two or more entities of a certain type (e.g. Person, Organisation, Location) and fall into a number of semantic categories (e.g. married to, employed by, lives in). | |
C5981 | Quartiles let us quickly divide a set of data into four groups, making it easy to see which of the four groups a particular data point is in. For example, a professor has graded an exam from 0-100 points. | |
C5982 | The main hyperparameter of the SVM is the kernel. It maps the observations into some feature space. The choice of the kernel and their hyperparameters affect greatly the separability of the classes (in classification) and the performance of the algorithm. | |
C5983 | With "infinite" numbers of successive random samples, the mean of the sampling distribution is equal to the population mean (µ). As the sample sizes increase, the variability of each sampling distribution decreases so that they become increasingly more leptokurtic. | |
C5984 | The coefficients used in simple linear regression can be found using stochastic gradient descent. Linear regression does provide a useful exercise for learning stochastic gradient descent which is an important algorithm used for minimizing cost functions by machine learning algorithms. | |
C5985 | A convolutional layer acts as a fully connected layer between a 3D input and output. The input is the “window” of pixels with the channels as depth. This is the same with the output considered as a 1 by 1 pixel “window”. The kernel size of a convolutional layer is k_w * k_h * c_in * c_out. | |
C5986 | Top 10 real-life examples of Machine LearningImage Recognition. Image recognition is one of the most common uses of machine learning. Speech Recognition. Speech recognition is the translation of spoken words into the text. Medical diagnosis. Statistical Arbitrage. Learning associations. Classification. Prediction. Extraction.More items• | |
C5987 | Always remember ReLu should be only used in hidden layers. For classification, Sigmoid functions(Logistic, tanh, Softmax) and their combinations work well. But at the same time, it may suffer from vanishing gradient problem. | |
C5988 | To assess which word2vec model is best, simply calculate the distance for each pair, do it 200 times, sum up the total distance, and the smallest total distance will be your best model. | |
C5989 | Here are some of the top reasons why you should multitask.Keeps You Active. When doing a simple task, like maybe texting an important message on your phone, you can easily get distracted by various thoughts. Tonic for the Brain. Need of the Hour in this Fast-Changing world. It is a Personality Trait. | |
C5990 | So the probability that the sample mean will be >22 is the probability that Z is > 1.6 We use the Z table to determine this: P( > 22) = P(Z > 1.6) = 0.0548. | |
C5991 | Q-learning is called off-policy because the updated policy is different from the behavior policy, so Q-Learning is off-policy. In other words, it estimates the reward for future actions and appends a value to the new state without actually following any greedy policy. | |
C5992 | AI taking into account many levels of abstraction, embodied AI and multimodal interaction is also DAI. Distributed AI means AI solved by multiple smart or reasoning agents (communicant object, physical or software) where size of agents can be a simple rule or can be a human or more ambient or pervasive structure. | |
C5993 | Prior probability represents what is originally believed before new evidence is introduced, and posterior probability takes this new information into account. A posterior probability can subsequently become a prior for a new updated posterior probability as new information arises and is incorporated into the analysis. | |
C5994 | One of the primary foundations of machine learning is data mining. Data mining can be used to extract more accurate data. This ultimately helps refine your machine learning to achieve better results. | |
C5995 | In short, tokenization uses a token to protect the data, whereas encryption uses a key. To access the original data, a tokenization solution exchanges the token for the sensitive data, and an encryption solution decodes the encrypted data to reveal its sensitive form. | |
C5996 | Regression Analysis > Forward selection is a type of stepwise regression which begins with an empty model and adds in variables one by one. In each forward step, you add the one variable that gives the single best improvement to your model. | |
C5997 | Inferential statistics lets you draw conclusions about populations by using small samples. Consequently, inferential statistics provide enormous benefits because typically you can't measure an entire population. | |
C5998 | The decomposition of time series is a statistical task that deconstructs a time series into several components, each representing one of the underlying categories of patterns. There are two principal types of decomposition, which are outlined below. | |
C5999 | Big data analysis caters to a large amount of data set which is also known as data mining, but data science makes use of the machine learning algorithms to design and develop statistical models to generate knowledge from the pile of big data. |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.