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C3000 | Regression analysis is used when you want to predict a continuous dependent variable from a number of independent variables. If the dependent variable is dichotomous, then logistic regression should be used. | |
C3001 | The optimal number of clusters can be defined as follow: Compute clustering algorithm (e.g., k-means clustering) for different values of k. For instance, by varying k from 1 to 10 clusters. For each k, calculate the total within-cluster sum of square (wss). | |
C3002 | Statisticians define two types of errors in hypothesis testing. Creatively, they call these errors Type I and Type II errors. Both types of error relate to incorrect conclusions about the null hypothesis. The table summarizes the four possible outcomes for a hypothesis test. | |
C3003 | A ratio scale is a quantitative scale where there is a true zero and equal intervals between neighboring points. Unlike on an interval scale, a zero on a ratio scale means there is a total absence of the variable you are measuring. Length, area, and population are examples of ratio scales. | |
C3004 | Consider using a portfolio of technical tools, as well as operational practices such as internal “red teams,” or third-party audits. Third, engage in fact-based conversations around potential human biases. | |
C3005 | Must-Know: How to evaluate a binary classifierTrue Positive Rate (TPR) or Hit Rate or Recall or Sensitivity = TP / (TP + FN)False Positive Rate(FPR) or False Alarm Rate = 1 - Specificity = 1 - (TN / (TN + FP))Accuracy = (TP + TN) / (TP + TN + FP + FN)Error Rate = 1 – accuracy or (FP + FN) / (TP + TN + FP + FN)Precision = TP / (TP + FP)More items | |
C3006 | An example of dimensionality reduction: email classification. Let's set up a specific example to illustrate how PCA works. Assume that you have a database of emails and you want to classify (using some machine learning numerical algorithm) each email as spam/not spam. | |
C3007 | This powerful technique is no longer constrained by the limits of human knowledge. Instead, the computer program accumulated thousands of years of human knowledge during a period of just a few days and learned to play Go from the strongest player in the world, AlphaGo. | |
C3008 | Training a model simply means learning (determining) good values for all the weights and the bias from labeled examples. In supervised learning, a machine learning algorithm builds a model by examining many examples and attempting to find a model that minimizes loss; this process is called empirical risk minimization. | |
C3009 | Info-gap decision theory is a non-probabilistic decision theory that seeks to optimize robustness to failure – or opportuneness for windfall – under severe uncertainty, in particular applying sensitivity analysis of the stability radius type to perturbations in the value of a given estimate of the parameter of interest | |
C3010 | Smaller MSE generally indicates a better estimate, at the data points in question. As others have said, MSE is the mean of the squared difference between your estimate and the data. Smaller MSE generally indicates a better estimate, at the data points in question. | |
C3011 | The NLP Engine is the core component that interprets what users say at any given time and converts that language to structured inputs the system can process. To interpret the user inputs, NLP engines, based on the business case, use either finite state automata models or deep learning methods. | |
C3012 | Validity is important because it can help determine what types of tests to use, and help to make sure researchers are using methods that are not only ethical, and cost-effective, but also a method that truly measures the idea or constructs in question. | |
C3013 | Data Augmentation in play. A convolutional neural network that can robustly classify objects even if its placed in different orientations is said to have the property called invariance. More specifically, a CNN can be invariant to translation, viewpoint, size or illumination (Or a combination of the above). | |
C3014 | You can use reinforcement learning for classification problems but it won't be giving you any added benefit and instead slow down your convergence rate. Detailed answer: yes but it's an overkill. So, if you possess labels, it would be a LOT more faster and easier to use regular supervised learning. | |
C3015 | A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. | |
C3016 | Most recent answer One way to compare the two different size data sets is to divide the large set into an N number of equal size sets. The comparison can be based on absolute sum of of difference. THis will measure how many sets from the Nset are in close match with the single 4 sample set. | |
C3017 | This result is known as Graham's law of diffusion after Thomas Graham (1805 to 1869), a Scottish chemist, who discovered it by observing effusion of gases through a thin plug of plaster of paris. Calculate the relative rates of effusion of He(g) and O2(g) . | |
C3018 | A discrete quantitative variable is one that can only take specific numeric values (rather than any value in an interval), but those numeric values have a clear quantitative interpretation. Examples of discrete quantitative variables are number of needle punctures, number of pregnancies and number of hospitalizations. | |
C3019 | Training Set: this data set is used to adjust the weights on the neural network. Validation Set: this data set is used to minimize overfitting. Testing Set: this data set is used only for testing the final solution in order to confirm the actual predictive power of the network. | |
C3020 | Importance sampling is a useful technique for investigating the properties of a distri- bution while only having samples drawn from a different (proposal) distribution. | |
C3021 | A Binomial Regression model can be used to predict the odds of an event. The Logistic Regression model is a special case of the Binomial Regression model in the situation where the size of each group of explanatory variables in the data set is one. | |
C3022 | The output of an LSTM cell or layer of cells is called the hidden state. This is confusing, because each LSTM cell retains an internal state that is not output, called the cell state, or c. | |
C3023 | In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a probability distribution by maximizing a likelihood function, so that under the assumed statistical model the observed data is most probable. | |
C3024 | The backpropagation algorithm works by computing the gradient of the loss function with respect to each weight by the chain rule, computing the gradient one layer at a time, iterating backward from the last layer to avoid redundant calculations of intermediate terms in the chain rule; this is an example of dynamic | |
C3025 | Simple random sampling: By using the random number generator technique, the researcher draws a sample from the population called simple random sampling. Simple random samplings are of two types. Cluster sampling: Cluster sampling occurs when a random sample is drawn from certain aggregational geographical groups. | |
C3026 | Fractional scaling helps you to fully utilize your HiDPI monitors, high-resolution laptops by making your desktop not too small or not too big and keep things in balance. Although the resolution settings are there to help they sometimes are not feasible due to the operating system limitations. | |
C3027 | Latent class regression, where the purpose of the analysis is to identify segments that contain different parameters. This model is most commonly used for creating segments with choice modeling data. Model-based clustering, where a series of numeric, categorical or ranking variables are used to create segments. | |
C3028 | The output of the network is a single vector (also with 10,000 components) containing, for every word in our vocabulary, the probability that a randomly selected nearby word is that vocabulary word. In word2vec, a distributed representation of a word is used. | |
C3029 | Enneagram test results are very accurate for determining your enneagram type and the MBTI test results are quite accurate for determining your MBTI type. Neither is in competition with the other. That being said, it can be very interesting to have the results for both of these uniquely different typologies. | |
C3030 | The result is that the coefficient estimates are unstable and difficult to interpret. Multicollinearity saps the statistical power of the analysis, can cause the coefficients to switch signs, and makes it more difficult to specify the correct model. | |
C3031 | The law of averages is the commonly held belief that a particular outcome or event will over certain periods of time occur at a frequency that is similar to its probability. Depending on context or application it can be considered a valid common-sense observation or a misunderstanding of probability. | |
C3032 | Memorization and generalization are both important for recommender systems. Wide linear models can effectively memorize sparse feature interactions using cross-product fea- ture transformations, while deep neural networks can gener- alize to previously unseen feature interactions through low- dimensional embeddings. | |
C3033 | 2:055:17Suggested clip · 113 secondsWeighted Kappa in IBM SPSS Statistics - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C3034 | A more accurate model postulates that the relative growth rate P /P decreases when P approaches the carrying capacity K of the environment. The corre- sponding equation is the so called logistic differential equation: dP dt = kP ( 1 − P K ) . | |
C3035 | Structural equation models are often used to assess unobservable 'latent' constructs. They often invoke a measurement model that defines latent variables using one or more observed variables, and a structural model that imputes relationships between latent variables. | |
C3036 | While the current state-of-the-art method for federated learning, FedAvg (McMahan et al, 2017), has demonstrated empirical success, it does not fully address the underlying challenges associated with heterogeneity, and can diverge in practice. | |
C3037 | To recap the differences between the two: Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned. Deep learning structures algorithms in layers to create an "artificial neural network” that can learn and make intelligent decisions on its own. | |
C3038 | Topic Modeling refers to the process of dividing a corpus of documents in two:A list of the topics covered by the documents in the corpus.Several sets of documents from the corpus grouped by the topics they cover. | |
C3039 | The coefficients in a linear-log model represent the estimated unit change in your dependent variable for a percentage change in your independent variable. The term on the right-hand-side is the percent change in X, and the term on the left-hand-side is the unit change in Y. | |
C3040 | Two events are dependent if the outcome of the first event affects the outcome of the second event, so that the probability is changed. | |
C3041 | Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. | |
C3042 | A q-value threshold of 0.05 yields a FDR of 5% among all features called significant. The q-value is the expected proportion of false positives among all features as or more extreme than the observed one. | |
C3043 | Subsampling reduces the image size by removing information all together. Usually when you subsample, you also interpolate or smooth the image so that you reduce aliasing. Usually, the chrominance values are filtered then subsampled by 1/2 or even 1/4 of that of the intensity. | |
C3044 | A fundamental difference between mean and median is that the mean is much more sensitive to extreme values than the median. That is, one or two extreme values can change the mean a lot but do not change the the median very much. Thus, the median is more robust (less sensitive to outliers in the data) than the mean. | |
C3045 | Logistic regression, also called a logit model, is used to model dichotomous outcome variables. In the logit model the log odds of the outcome is modeled as a linear combination of the predictor variables. | |
C3046 | The bits of linguistic information that enter into one person's mind, from another, cause people to entertain a new thought with profound effects on his world knowledge, inferencing, and subsequent behavior. Language neither creates nor distorts conceptual life. Thought comes first, while language is an expression. | |
C3047 | Activation functions are mathematical equations that determine the output of a neural network. The function is attached to each neuron in the network, and determines whether it should be activated (“fired”) or not, based on whether each neuron's input is relevant for the model's prediction. | |
C3048 | The idea behind importance sampling is that certain values of the input random variables in a simulation have more impact on the parameter being estimated than others. If these "important" values are emphasized by sampling more frequently, then the estimator variance can be reduced. | |
C3049 | Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking). | |
C3050 | Calculate the derivative of g(x)=ln(x2+1). Solution: To use the chain rule for this problem, we need to use the fact that the derivative of ln(z) is 1/z. Then, by the chain rule, the derivative of g is g′(x)=ddxln(x2+1)=1x2+1(2x)=2xx2+1. | |
C3051 | Interaction effects occur when the effect of one variable depends on the value of another variable. Interaction effects are common in regression analysis, ANOVA, and designed experiments. Interaction effects indicate that a third variable influences the relationship between an independent and dependent variable. | |
C3052 | It depends on the data you want and the project you're doing. You could use even your twitter data for sentiment analysis. Request your archive in twitter -> download -> analyse sentiment through supervised learning techniques. | |
C3053 | The Least Squares Regression Line is the line that makes the vertical distance from the data points to the regression line as small as possible. It's called a “least squares” because the best line of fit is one that minimizes the variance (the sum of squares of the errors). | |
C3054 | Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. | |
C3055 | The bag-of-words model is a simplifying representation used in natural language processing and information retrieval (IR). In this model, a text (such as a sentence or a document) is represented as the bag (multiset) of its words, disregarding grammar and even word order but keeping multiplicity. | |
C3056 | In other words, discriminative models are used to specify outputs based on inputs (by models such as Logistic regression, Neural networks and Random forests), while generative models generate both inputs and outputs (for example, by Hidden Markov model, Bayesian Networks and Gaussian mixture model). | |
C3057 | Prior probability, in Bayesian statistical inference, is the probability of an event before new data is collected. | |
C3058 | By reversing the words in the source sentence, the average distance between corresponding words in the source and target language is unchanged. However, the first few words in the source language are now very close to the first few words in the target language, so the problem's minimal time lag is greatly reduced. | |
C3059 | Additive interaction means the effect of two chemicals is equal to the sum of the effect of the two chemicals taken separately. Synergistic interaction means that the effect of two chemicals taken together is greater than the sum of their separate effect at the same doses. | |
C3060 | Bivariate analysis means the analysis of bivariate data. It is one of the simplest forms of statistical analysis, used to find out if there is a relationship between two sets of values. It usually involves the variables X and Y. Univariate analysis is the analysis of one (“uni”) variable. | |
C3061 | Typically, a regression analysis is done for one of two purposes: In order to predict the value of the dependent variable for individuals for whom some information concerning the explanatory variables is available, or in order to estimate the effect of some explanatory variable on the dependent variable. | |
C3062 | Genetic algorithm is used in optimum design because of its efficient optimum capabilities. The genetic algorithm is an efficient tool in the field of engineering education (Bütün, 2005). | |
C3063 | By using some mathematics it can be shown that there are a few conditions that we need to use a normal approximation to the binomial distribution. The number of observations n must be large enough, and the value of p so that both np and n(1 - p) are greater than or equal to 10. | |
C3064 | A Type I is a false positive where a true null hypothesis that there is nothing going on is rejected. A Type II error is a false negative, where a false null hypothesis is not rejected – something is going on – but we decide to ignore it. | |
C3065 | Semantic similarity: this scores words based on how similar they are, even if they are not exact matches. It borrows techniques from Natural Language Processing (NLP), such as word embeddings. | |
C3066 | Data bias in machine learning is a type of error in which certain elements of a dataset are more heavily weighted and/or represented than others. A biased dataset does not accurately represent a model's use case, resulting in skewed outcomes, low accuracy levels, and analytical errors. | |
C3067 | The normal distribution is the most important probability distribution in statistics because it fits many natural phenomena. For example, heights, blood pressure, measurement error, and IQ scores follow the normal distribution. | |
C3068 | Perhaps the biggest problem with using the historical LCGs for generating random numbers is that their periods are too short, even if they manage to hit the maximal period. Given the scale of simulations being conducted today, even a period of 232 would likely be too short to appear sufficiently random. | |
C3069 | In contrast to the non-stationary process that has a variable variance and a mean that does not remain near, or returns to a long-run mean over time, the stationary process reverts around a constant long-term mean and has a constant variance independent of time. | |
C3070 | Discriminant validity (or divergent validity) tests that constructs that should have no relationship do, in fact, not have any relationship. If a research program is shown to possess both of these types of validity, it can also be regarded as having excellent construct validity. | |
C3071 | The basic premise of transfer learning is simple: take a model trained on a large dataset and transfer its knowledge to a smaller dataset. For object recognition with a CNN, we freeze the early convolutional layers of the network and only train the last few layers which make a prediction. | |
C3072 | The Most Simple Ways to Build an Interactive Decision TreeLog in to your Zingtree account, go to My Trees and select Create New Tree. After naming your decision tree, choosing your ideal display style and providing a description, just click the Create Tree button to move on to the next step.More items• | |
C3073 | The basic idea behind a neural network is to simulate (copy in a simplified but reasonably faithful way) lots of densely interconnected brain cells inside a computer so you can get it to learn things, recognize patterns, and make decisions in a humanlike way. | |
C3074 | Another cause of skewness is start-up effects. For example, if a procedure initially has a lot of successes during a long start-up period, this could create a positive skew on the data. (On the opposite hand, a start-up period with several initial failures can negatively skew data.) | |
C3075 | When they are positively skewed (long right tail) taking logs can sometimes help. Sometimes logs are taken of the dependent variable, sometimes of one or more independent variables. Substantively, sometimes the meaning of a change in a variable is more multiplicative than additive. For example, income. | |
C3076 | If a problem is nonlinear and its class boundaries cannot be approximated well with linear hyperplanes, then nonlinear classifiers are often more accurate than linear classifiers. If a problem is linear, it is best to use a simpler linear classifier. | |
C3077 | The mean is also to the left of the peak. A right-skewed distribution has a long right tail. Next, you'll see a fair amount of negatively skewed distributions. For example, household income in the U.S. is negatively skewed with a very long left tail. | |
C3078 | DeepDream is an experiment that visualizes the patterns learned by a neural network. Similar to when a child watches clouds and tries to interpret random shapes, DeepDream over-interprets and enhances the patterns it sees in an image. | |
C3079 | It works in part because it doesn't require unbiased estimators; While least squares produces unbiased estimates, variances can be so large that they may be wholly inaccurate. Ridge regression adds just enough bias to make the estimates reasonably reliable approximations to true population values. | |
C3080 | The sensitivity of the test reflects the probability that the screening test will be positive among those who are diseased. In contrast, the specificity of the test reflects the probability that the screening test will be negative among those who, in fact, do not have the disease. | |
C3081 | It is named after Andrey Kolmogorov and Nikolai Smirnov. The Kolmogorov–Smirnov statistic quantifies a distance between the empirical distribution function of the sample and the cumulative distribution function of the reference distribution, or between the empirical distribution functions of two samples. | |
C3082 | When the standard deviation or the mean change, something unusual is happening. To detect such changes, for each upcoming point “p” we create of window from “p” to “p-100″. Then, we calculate the standard deviation and mean of this window. If it changes too much, an anomaly has been detected. | |
C3083 | RNN is recurrent in nature as it performs the same function for every input of data while the output of the current input depends on the past one computation. Unlike feed-forward neural networks, RNNs can use their internal state (memory) to process sequences of inputs. | |
C3084 | One standard deviation or one-sigma, plotted either above or below the average value, includes 68 percent of all data points. Two-sigma includes 95 percent and three-sigma includes 99.7 percent. Higher sigma values mean that the discovery is less and less likely to be accidentally a mistake or 'random chance'. | |
C3085 | Classification table. The classification table is another method to evaluate the predictive accuracy of the logistic regression model. In this table the observed values for the dependent outcome and the predicted values (at a user defined cut-off value, for example p=0.50) are cross-classified. | |
C3086 | Deep learning really shines when it comes to complex tasks, which often require dealing with lots of unstructured data, such as image classification, natural language processing, or speech recognition, among others. | |
C3087 | 7 Techniques to Handle Imbalanced DataUse 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. | |
C3088 | Because data science is a broad term for multiple disciplines, machine learning fits within data science. Machine learning uses various techniques, such as regression and supervised clustering. On the other hand, the data' in data science may or may not evolve from a machine or a mechanical process. | |
C3089 | A decision tree is a simple representation for classifying examples. Decision tree learning is one of the most successful techniques for supervised classification learning. A decision tree or a classification tree is a tree in which each internal (non-leaf) node is labeled with an input feature. | |
C3090 | Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data. | |
C3091 | In simple terms, deep learning is when ANNs learn from large amounts of data. Similar to how humans learn from experience, a deep learning algorithm performs a task repeatedly, each time tweaking it slightly to improve the outcome. | |
C3092 | Weights control the signal (or the strength of the connection) between two neurons. In other words, a weight decides how much influence the input will have on the output. Biases, which are constant, are an additional input into the next layer that will always have the value of 1. | |
C3093 | It is one of several methods statisticians and researchers use to extract a sample from a larger population; other methods include stratified random sampling and probability sampling. The advantages of a simple random sample include its ease of use and its accurate representation of the larger population. | |
C3094 | Definition. Univariate analyses are used extensively in quality of life research. Univariate analysis is defined as analysis carried out on only one (“uni”) variable (“variate”) to summarize or describe the variable (Babbie, 2007; Trochim, 2006). | |
C3095 | Interaction effects occur when the effect of one variable depends on the value of another variable. Interaction effects are common in regression analysis, ANOVA, and designed experiments. Interaction effects indicate that a third variable influences the relationship between an independent and dependent variable. | |
C3096 | Vanishing Gradient problem arises while training an Artificial Neural Network. This mainly occurs when the network parameters and hyperparameters are not properly set. Parameters could be weights and biases while hyperparameters could be learning rate, the number of epochs, the number of batches, etc. | |
C3097 | For example, a perfect precision and recall score would result in a perfect F-Measure score:F-Measure = (2 * Precision * Recall) / (Precision + Recall)F-Measure = (2 * 1.0 * 1.0) / (1.0 + 1.0)F-Measure = (2 * 1.0) / 2.0.F-Measure = 1.0. | |
C3098 | Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. | |
C3099 | The geometric mean must be used when working with percentages, which are derived from values, while the standard arithmetic mean works with the values themselves. The harmonic mean is best used for fractions such as rates or multiples. |
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