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C6400 | Spectral analysis is a technique that allows us to discover underlying periodicities. To perform spectral analysis, we first must transform data from time domain to frequency domain. | |
C6401 | Common examples of algorithms with coefficients that can be optimized using gradient descent are Linear Regression and Logistic Regression. | |
C6402 | Therefore, a number of alternative ways of handling the missing data has been developed.Listwise or case deletion. Pairwise deletion. Mean substitution. Regression imputation. Last observation carried forward. Maximum likelihood. Expectation-Maximization. Multiple imputation.More items• | |
C6403 | Type I error is equivalent to a False positive. Type II error is equivalent to a False negative. Type I error refers to non-acceptance of hypothesis which ought to be accepted. Type II error is the acceptance of hypothesis which ought to be rejected. | |
C6404 | Uses. Quota sampling is useful when time is limited, a sampling frame is not available, the research budget is very tight or detailed accuracy is not important. Subsets are chosen and then either convenience or judgment sampling is used to choose people from each subset. | |
C6405 | It's O(V+E) because each visit to v of V must visit each e of E where |e| <= V-1. Since there are V visits to v of V then that is O(V). So total time complexity is O(V + E). | |
C6406 | Having more data is always a good idea. It allows the “data to tell for itself,” instead of relying on assumptions and weak correlations. Presence of more data results in better and accurate models. For example: we do not get a choice to increase the size of training data in data science competitions. | |
C6407 | Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve. | |
C6408 | If the data set follows the bias then Naive Bayes will be a better classifier. Both Naive Bayes and Logistic regression are linear classifiers, Logistic Regression makes a prediction for the probability using a direct functional form where as Naive Bayes figures out how the data was generated given the results. | |
C6409 | A correlation matrix is a table showing correlation coefficients between variables. Each cell in the table shows the correlation between two variables. A correlation matrix is used to summarize data, as an input into a more advanced analysis, and as a diagnostic for advanced analyses. | |
C6410 | Findings. A fundamental problem with stepwise regression is that some real explanatory variables that have causal effects on the dependent variable may happen to not be statistically significant, while nuisance variables may be coincidentally significant. | |
C6411 | Below are the different regression techniques: Ridge Regression. Lasso Regression. Polynomial Regression. Bayesian Linear Regression. | |
C6412 | A type III error is where you correctly reject the null hypothesis, but it's rejected for the wrong reason. This compares to a Type I error (incorrectly rejecting the null hypothesis) and a Type II error (not rejecting the null when you should). | |
C6413 | Dimensional Analysis (also called Factor-Label Method or the Unit Factor Method) is a problem-solving method that uses the fact that any number or expression can be multiplied by one without changing its value. It is a useful technique. | |
C6414 | Figure 14.11: A nonlinear problem. An example of a nonlinear classifier is kNN. Linear classifiers misclassify the enclave, whereas a nonlinear classifier like kNN will be highly accurate for this type of problem if the training set is large enough. | |
C6415 | A test statistic is a statistic (a quantity derived from the sample) used in statistical hypothesis testing. An important property of a test statistic is that its sampling distribution under the null hypothesis must be calculable, either exactly or approximately, which allows p-values to be calculated. | |
C6416 | The probability of observing any single value is equal to 0, since the number of values which may be assumed by the random variable is infinite. | |
C6417 | The BIR is based on Boolean logic and classical set theory in that both the documents to be searched and the user's query are conceived as sets of terms (a bag-of-words model). Retrieval is based on whether or not the documents contain the query terms. | |
C6418 | 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). | |
C6419 | The Chi-square test is a non-parametric statistic, also called a distribution free test. Non-parametric tests should be used when any one of the following conditions pertains to the data: The level of measurement of all the variables is nominal or ordinal. | |
C6420 | Share: Filters are systems or elements used to remove substances such as dust or dirt, or electronic signals, etc., as they pass through filtering media or devices. Filters are available for filtering air or gases, fluids, as well as electrical and optical phenomena. Air filters are used for cleaning air. | |
C6421 | There are four steps in training and using the sentiment classifier:Load a pretrained word embedding.Load an opinion lexicon listing positive and negative words.Train a sentiment classifier using the word vectors of the positive and negative words.Calculate the mean sentiment scores of the words in a piece of text. | |
C6422 | Random forests consist of multiple single trees each based on a random sample of the training data. They are typically more accurate than single decision trees. The following figure shows the decision boundary becomes more accurate and stable as more trees are added. | |
C6423 | A matrix with rows and columns over a field is a function from the set of all ordered pairs of integers in range to . A linear operator is a linear function from a Vector space to itself. In notations, given a vector space , a linear operator is a function which satisfies for all in the underlying Field and vectors . | |
C6424 | Correlated vs. A correlated subquery can be thought of as a filter on the table that it refers to, as if the subquery were evaluated on each row of the table in the outer query. An uncorrelated subquery has no such external column references. | |
C6425 | By taking advantage of naturally occurring structure, we can design learning algorithms that exhaustively search even infinite hypothesis spaces without explicitly enumerating every hypothesis. For instance, general-to-specific ordering. | |
C6426 | a. it allows us to disregard the size of the sample selected when the population is not normal. it allows us the disregard the shape of the population when n is large. | |
C6427 | Regression is primarily used to build models/equations to predict a key response, Y, from a set of predictor (X) variables. Correlation is primarily used to quickly and concisely summarize the direction and strength of the relationships between a set of 2 or more numeric variables. | |
C6428 | Face detection is a broader term than face recognition. Face detection just means that a system is able to identify that there is a human face present in an image or video. Face recognition can confirm identity. It is therefore used to control access to sensitive areas. | |
C6429 | ReLU is an activation function, that nullifies negative neurons, and in its simplicity, it also aids computation speed. However, unlike ELU, it doesn't have a normalizing effect, so BatchNorm helps even better. | |
C6430 | AI programs can provide automation for low-value tasks freeing up engineers to perform higher-value tasks. By using machine learning to discover patterns in the data, machines will be incredibly important to help with engineering judgment. | |
C6431 | Multiclass classification with logistic regression can be done either through the one-vs-rest scheme in which for each class a binary classification problem of data belonging or not to that class is done, or changing the loss function to cross- entropy loss. | |
C6432 | SGD is a variant of gradient descent. Instead of performing computations on the whole dataset — which is redundant and inefficient — SGD only computes on a small subset or random selection of data examples. Essentially Adam is an algorithm for gradient-based optimization of stochastic objective functions. | |
C6433 | Decision trees are mainly used to perform classification tasks. Samples are submitted to a test in each node of the tree and guided through the tree based on the result. Decision trees can also be used to perform clustering, with a few adjustments. | |
C6434 | A linear threshold unit is a simple artificial neuron whose output is its thresholded total net input. That is, an LTU with threshold T calculates the weighted sum of its inputs, and then outputs 0 if this sum is less than T, and 1 if the sum is greater than T. | |
C6435 | 1.96 | |
C6436 | 1) Your model performs better on the training data than on the unknown validation data. It can also happen when your training loss is calculated as a moving average over 1 epoch, whereas the validation loss is calculated after the learning phase of the same epoch. | |
C6437 | Target variable, in the machine learning context is the variable that is or should be the output. For example it could be binary 0 or 1 if you are classifying or it could be a continuous variable if you are doing a regression. In statistics you also refer to it as the response variable. | |
C6438 | Bootstrap aggregating (bagging) In order to promote model variance, bagging trains each model in the ensemble using a randomly drawn subset of the training set. As an example, the random forest algorithm combines random decision trees with bagging to achieve very high classification accuracy. | |
C6439 | Image recognition is classifying data into one bucket out of many.Steps in the processgather and organize data to work with (85% of the effort)build and test a predictive model (10% of the effort)use the model to recognize images (5% of the effort) | |
C6440 | Clustering starts by computing a distance between every pair of units that you want to cluster. A distance matrix will be symmetric (because the distance between x and y is the same as the distance between y and x) and will have zeroes on the diagonal (because every item is distance zero from itself). | |
C6441 | With inferential statistics, you take data from samples and make generalizations about a population. For example, you might stand in a mall and ask a sample of 100 people if they like shopping at Sears. This is where you can use sample data to answer research questions. | |
C6442 | At a very basic level, deep learning is a machine learning technique. It teaches a computer to filter inputs through layers to learn how to predict and classify information. Observations can be in the form of images, text, or sound. The inspiration for deep learning is the way that the human brain filters information. | |
C6443 | Tokenization is a common task in Natural Language Processing (NLP). Tokenization is a way of separating a piece of text into smaller units called tokens. Here, tokens can be either words, characters, or subwords. | |
C6444 | 95% | |
C6445 | A common problem in machine learning is sparse data, which alters the performance of machine learning algorithms and their ability to calculate accurate predictions. Data is considered sparse when certain expected values in a dataset are missing, which is a common phenomenon in general large scaled data analysis. | |
C6446 | Bootstrap Confidence IntervalsCalculate a Population of Statistics. The first step is to use the bootstrap procedure to resample the original data a number of times and calculate the statistic of interest. Calculate Confidence Interval. Now that we have a population of the statistics of interest, we can calculate the confidence intervals. | |
C6447 | Multicollinearity exists whenever an independent variable is highly correlated with one or more of the other independent variables in a multiple regression equation. Multicollinearity is a problem because it undermines the statistical significance of an independent variable. | |
C6448 | Fortunately, hinge loss, logistic loss and square loss are all convex functions. Convexity ensures global minimum and it's computationally appleaing. | |
C6449 | The median is a simple measure of central tendency. To find the median, we arrange the observations in order from smallest to largest value. If there is an odd number of observations, the median is the middle value. If there is an even number of observations, the median is the average of the two middle values. | |
C6450 | In a hypothesis test, we:Evaluate the null hypothesis, typically denoted with H0. Always write the alternative hypothesis, typically denoted with Ha or H1, using less than, greater than, or not equals symbols, i.e., (≠, >, or <).More items | |
C6451 | Topic modelling refers to the task of identifying topics that best describes a set of documents. And the goal of LDA is to map all the documents to the topics in a way, such that the words in each document are mostly captured by those imaginary topics. | |
C6452 | The advantage with Wilcoxon Signed Rank Test is that it neither depends on the form of the parent distribution nor on its parameters. It does not require any assumptions about the shape of the distribution. | |
C6453 | A baseline is a method that uses heuristics, simple summary statistics, randomness, or machine learning to create predictions for a dataset. A machine learning algorithm tries to learn a function that models the relationship between the input (feature) data and the target variable (or label). | |
C6454 | Discrete data involves round, concrete numbers that are determined by counting. Continuous data involves complex numbers that are measured across a specific time interval. | |
C6455 | Imitation Learning (IL) and Reinforcement Learning (RL) are often introduced as similar, but separate problems. Imitation learning involves a supervisor that provides data to the learner. Reinforcement learning means the agent has to explore in the environment to get feedback signals. | |
C6456 | The larger the sample size is the smaller the effect size that can be detected. The reverse is also true; small sample sizes can detect large effect sizes. Thus an appropriate determination of the sample size used in a study is a crucial step in the design of a study. | |
C6457 | Friedman Test Therefore, we have a non-parametric equivalent of the two way ANOVA that can be used for data sets which do not fulfill the assumptions of the parametric method. The method, which is sometimes known as Friedman's two way analysis of variance, is purely a hypothesis test. | |
C6458 | Deep nets are hard to train because of the presence of multiple local minimas. The optimization problem for a deep net is non-convex, hence, has multiple local minimas. If any of the chosen hyper-parameters are not appropriate, you will end up at a bad local minima which will lead to poor performance. | |
C6459 | There are often only a handful of possible classes or results. For a given classification, one tries to measure the probability of getting different evidence or patterns. Using Bayes rule, we use this to get what is desired, the conditional probability of the classification given the evidence. | |
C6460 | The technique of Monte Carlo Simulation (MCS) was originally developed for use in nuclear weapons design. It provides an efficient way to simulate processes involving chance and uncertainty and can be applied in areas as diverse as market sizing, customer lifetime value measurement and customer service management. | |
C6461 | A feedforward neural network is an artificial neural network wherein connections between the units do not form a cycle. The simplest kind of neural network is a single-layer perceptron network, which consists of a single layer of output nodes; the inputs are fed directly to the outputs via a series of weights. | |
C6462 | Use regression analysis to describe the relationships between a set of independent variables and the dependent variable. Regression analysis produces a regression equation where the coefficients represent the relationship between each independent variable and the dependent variable. | |
C6463 | Support Vector Machine can also be used as a regression method, maintaining all the main features that characterize the algorithm (maximal margin). The Support Vector Regression (SVR) uses the same principles as the SVM for classification, with only a few minor differences. | |
C6464 | As for exponential smoothing, also ARIMA models are among the most widely used approaches for time series forecasting. The name is an acronym for AutoRegressive Integrated Moving Average. In an AutoRegressive model the forecasts correspond to a linear combination of past values of the variable. | |
C6465 | The linear function is called the objective function , of the form f(x,y)=ax+by+c . | |
C6466 | The convolutional layer is the core building block of a CNN. The layer's parameters consist of a set of learnable filters (or kernels), which have a small receptive field, but extend through the full depth of the input volume. | |
C6467 | Clustering is the most commonly used unsupervised learning method. This is because typically it is one of the best ways to explore and find out more about data visually. k-Means clustering: partitions data into k distinct clusters based on distance to the centroid of a cluster. | |
C6468 | Clustering methods are used to identify groups of similar objects in a multivariate data sets collected from fields such as marketing, bio-medical and geo-spatial. They are different types of clustering methods, including: Partitioning methods. Hierarchical clustering. | |
C6469 | When we say that correlation does not imply cause, we mean that just because you can see a connection or a mutual relationship between two variables, it doesn't necessarily mean that one causes the other. | |
C6470 | In statistics, single-linkage clustering is one of several methods of hierarchical clustering. It is based on grouping clusters in bottom-up fashion (agglomerative clustering), at each step combining two clusters that contain the closest pair of elements not yet belonging to the same cluster as each other. | |
C6471 | Assuming 0<σ2<∞, by definition σ2=E[(X−μ)2]. Thus, the variance itself is the mean of the random variable Y=(X−μ)2. This suggests the following estimator for the variance ˆσ2=1nn∑k=1(Xk−μ)2. | |
C6472 | The median is calculated by first sorting all the pixel values from the window into numerical order, and then replacing the pixel being considered with the middle (median) pixel value. | |
C6473 | Systematic sampling is a type of probability sampling method in which sample members from a larger population are selected according to a random starting point but with a fixed, periodic interval. This interval, called the sampling interval, is calculated by dividing the population size by the desired sample size. | |
C6474 | F-test is statistical test, that determines the equality of the variances of the two normal populations. T-statistic follows Student t-distribution, under null hypothesis. F-statistic follows Snedecor f-distribution, under null hypothesis. Comparing the means of two populations. | |
C6475 | The principal advantage of linear regression is its simplicity, interpretability, scientific acceptance, and widespread availability. Linear regression is the first method to use for many problems. Analysts can use linear regression together with techniques such as variable recoding, transformation, or segmentation. | |
C6476 | An algorithm that uses random numbers to decide what to do next anywhere in its logic is called Randomized Algorithm. For example, in Randomized Quick Sort, we use random number to pick the next pivot (or we randomly shuffle the array). | |
C6477 | The posterior distribution is a way to summarize what we know about uncertain quantities in Bayesian analysis. It is a combination of the prior distribution and the likelihood function, which tells you what information is contained in your observed data (the “new evidence”). | |
C6478 | A statistical test provides a mechanism for making quantitative decisions about a process or processes. The intent is to determine whether there is enough evidence to "reject" a conjecture or hypothesis about the process. | |
C6479 | ratio() to measure similarity between two strings. Pass two strings into difflib. SequenceMatcher(isjunk, a, b) with isJunk set to None to get a SequenceMatcher() object representing the similarity between the strings. Call ratio() on this object to get the ratio of matching characters to total characters. | |
C6480 | Correlation means association - more precisely it is a measure of the extent to which two variables are related. There are three possible results of a correlational study: a positive correlation, a negative correlation, and no correlation. | |
C6481 | Cohen suggested the Kappa result be interpreted as follows: values ≤ 0 as indicating no agreement and 0.01–0.20 as none to slight, 0.21–0.40 as fair, 0.41– 0.60 as moderate, 0.61–0.80 as substantial, and 0.81–1.00 as almost perfect agreement. | |
C6482 | NLP is a technological process that allows computers to derive meaning from user text inputs. With NLP, you are able to “train” your chatbot on the various interactions it will go through, and help streamline the responses it outputs. | |
C6483 | In machine learning we are trying to create solutions to some problem by using data or examples. Genetic algorithms are stochastic search algorithms which are often used in machine learning applications. | |
C6484 | Ordinary least squares (OLS) regression is a statistical method of analysis that estimates the relationship between one or more independent variables and a dependent variable; the method estimates the relationship by minimizing the sum of the squares in the difference between the observed and predicted values of the | |
C6485 | Here we have few types of classification algorithms in machine learning:Linear Classifiers: Logistic Regression, Naive Bayes Classifier.Nearest Neighbor.Support Vector Machines.Decision Trees.Boosted Trees.Random Forest.Neural Networks. | |
C6486 | Neural networks are often compared to decision trees because both methods can model data that has nonlinear relationships between variables, and both can handle interactions between variables. A neural network is more of a “black box” that delivers results without an explanation of how the results were derived. | |
C6487 | The least squares criterion is a formula used to measure the accuracy of a straight line in depicting the data that was used to generate it. That is, the formula determines the line of best fit. This mathematical formula is used to predict the behavior of the dependent variables. | |
C6488 | The Hopfield Network. The nodes of a Hopfield network can be updated synchronously or asynchronously. Synchronous updating means that at time step (t+1) every neuron is updated based on the network state at time step t. | |
C6489 | 2.1 The Early Days. Constraint satisfaction, in its basic form, involves finding a value for each one of a set of problem variables where constraints specify that some subsets of values cannot be used together. | |
C6490 | Probability theory is the mathematical study of phenomena characterized by randomness or uncertainty. More precisely, probability is used for modelling situations when the result of an experiment, realized under the same circumstances, produces different results (typically throwing a dice or a coin). | |
C6491 | To calculate Maddrey discriminant function using SI units, such as micromoles per litre, divide bilirubin value by 17. | |
C6492 | In terms of linear regression, variance is a measure of how far observed values differ from the average of predicted values, i.e., their difference from the predicted value mean. The goal is to have a value that is low. | |
C6493 | In other words, accuracy describes the difference between the measurement and the part's actual value, while precision describes the variation you see when you measure the same part repeatedly with the same device. | |
C6494 | Class limits are the least and greatest numbers that can belong to the class. Class boundaries are the numbers that separate classes without forming gaps between them. | |
C6495 | Type 1 error, in statistical hypothesis testing, is the error caused by rejecting a null hypothesis when it is true. Type II error is the error that occurs when the null hypothesis is accepted when it is not true. | |
C6496 | The point of a test set is to give you a final, unbiased performance measure of your entire model building process. | |
C6497 | The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. | |
C6498 | Use Augmented Dickey-Fuller Test (adf test). A p-Value of less than 0.05 in adf. test() indicates that it is stationary. | |
C6499 | Linear Discriminant Analysis or Normal Discriminant Analysis or Discriminant Function Analysis is a dimensionality reduction technique which is commonly used for the supervised classification problems. It is used for modeling differences in groups i.e. separating two or more classes. |
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