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C6000 | Here are 25 phases that you can use to increase confidence and self-esteem in your children.“You are capable." “That was brave." “You've got this." “I believe in you." “You can do hard things." “No matter what happens, I love you." “Let's try it together." “How'd you do that?"More items | |
C6001 | Hypothesis Testing — 2-tailed testSpecify the Null(H0) and Alternate(H1) hypothesis.Choose the level of Significance(α)Find Critical Values.Find the test statistic.Draw your conclusion. | |
C6002 | t-test is used to test if two sample have the same mean. The assumptions are that they are samples from normal distribution. f-test is used to test if two sample have the same variance. Same assumptions hold. | |
C6003 | Independent and Identically Distributed | |
C6004 | Methods to Avoid Underfitting in Neural Networks—Adding Parameters, Reducing Regularization ParameterAdding neuron layers or input parameters. Adding more training samples, or improving their quality. Dropout. Decreasing regularization parameter. | |
C6005 | Inverse transform sampling is a method for generating random numbers from any probability distribution by using its inverse cumulative distribution F−1(x). Recall that the cumulative distribution for a random variable X is FX(x)=P(X≤x). | |
C6006 | Discriminative models, also referred to as conditional models or backward models, are a class of supervised machine learning used for classification or regression. These distinguish decision boundaries by inferring knowledge from observed data. | |
C6007 | Essentially, multivariate analysis is a tool to find patterns and relationships between several variables simultaneously. It lets us predict the effect a change in one variable will have on other variables. | |
C6008 | SVM or Support Vector Machine is a linear model for classification and regression problems. It can solve linear and non-linear problems and work well for many practical problems. The idea of SVM is simple: The algorithm creates a line or a hyperplane which separates the data into classes. | |
C6009 | The kernel parameter σ is sensitive to the one-class classification model with the Gaussian RBF Kernel. This sigma selection method uses a line search with an state-of-the-art objective function to find the optimal value. The kernel matrix is the bridge between σ and the model. | |
C6010 | Local features refer to a pattern or distinct structure found in an image, such as a point, edge, or small image patch. They are usually associated with an image patch that differs from its immediate surroundings by texture, color, or intensity. | |
C6011 | A recurrent neural network, however, is able to remember those characters because of its internal memory. It produces output, copies that output and loops it back into the network. Simply put: recurrent neural networks add the immediate past to the present. | |
C6012 | With this method people need to remember their target blood sugar level. Subtract the target blood sugar from the current sugar to calculate the gap. Then divide by the Correction (sensitivity) Factor to calculate the correction dose. Discuss your target levels with your health care team (see Question 1). | |
C6013 | Scalable Machine Learning occurs when Statistics, Systems, Machine Learning and Data Mining are combined into flexible, often nonparametric, and scalable techniques for analyzing large amounts of data at internet scale. | |
C6014 | Recurrent Neural Networks are best suited for Text Processing. | |
C6015 | If the level of significance is α = 0.10, then for a one tailed test the critical region is below z = -1.28 or above z = 1.28. For a two tailed test, use α/2 = 0.05 and the critical region is below z = -1.645 and above z = 1.645. | |
C6016 | Answer : Algorithm is a noun meaning some special process of solving a certain type of problem. Whereas logarithm, again a noun, is the exponent of that power of a fixed number, called the base, which equals a given number, called the antilogarithm. | |
C6017 | Regression is mainly used in order to make estimates or predictions for the dependent variable with the help of single or multiple independent variables, and ANOVA is used to find a common mean between variables of different groups. | |
C6018 | Probability and the Normal Curve The normal distribution is a continuous probability distribution. This has several implications for probability. The total area under the normal curve is equal to 1. The probability that a normal random variable X equals any particular value is 0. | |
C6019 | The power of a hypothesis test is affected by three factors. Sample size (n). Other things being equal, the greater the sample size, the greater the power of the test. The greater the difference between the "true" value of a parameter and the value specified in the null hypothesis, the greater the power of the test. | |
C6020 | The bag-of-words feature vector is the sum of all one-hot vectors of the words, and therefore has a non-zero value for every word that occurred. Continuous bag-of-words (CBOW) is exactly the same, but instead of using sparse vectors to represent words, it uses dense vectors (continuous distributional "embeddings"). | |
C6021 | In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. | |
C6022 | Markov analysis is a method used to forecast the value of a variable whose predicted value is influenced only by its current state, and not by any prior activity. Markov analysis is often used for predicting behaviors and decisions within large groups of people. | |
C6023 | DEFINITION: Primary sampling unit refers to Sampling units that are selected in the first (primary) stage of a multi-stage sample ultimately aimed at selecting individual elements. | |
C6024 | The main difference is that ABM typically implement low numbers of highly complex agents, and the main feature they consider are their individual capabilities to face the task. On the opposite, MAS consider (very) large numbers of simpler agents, focusing on the emergence of new phenomena from social interactions. | |
C6025 | A local vector has integer-typed and 0-based indices and double-typed values, stored on a single machine. MLlib supports two types of local vectors: dense and sparse. A dense vector is backed by a double array representing its entry values, while a sparse vector is backed by two parallel arrays: indices and values. | |
C6026 | In statistics, the bias (or bias function) of an estimator is the difference between this estimator's expected value and the true value of the parameter being estimated. Consistent estimators converge in probability to the true value of the parameter, but may be biased or unbiased; see bias versus consistency for more. | |
C6027 | In the future, artificial intelligence (AI) is likely to substantially change both marketing strategies and customer behaviors. Finally, the authors suggest AI will be more effective if it augments (rather than replaces) human managers. AI is going to make our lives better in the future. | |
C6028 | Distributed deep learning is a sub-area of general distributed machine learning that has recently become very prominent because of its effectiveness in various applications. | |
C6029 | The probability of Type 1 error is alpha -- the criterion that we set as the level at which we will reject the null hypothesis. The p value is something else -- it tells you how UNUSUAL the data are, given the assumption that the null hypothesis is true. | |
C6030 | SummaryWeighted Mean: A mean where some values contribute more than others.When the weights add to 1: just multiply each weight by the matching value and sum it all up.Otherwise, multiply each weight w by its matching value x, sum that all up, and divide by the sum of weights: Weighted Mean = ΣwxΣw. | |
C6031 | 8:3417:13Suggested clip · 72 secondsStepwise regression procedures in SPSS (new, 2018) - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C6032 | The Best Tools for Machine Learning Model VisualizationLook at evaluation metrics (also you should know how to choose an evaluation metric for your problem)Look at performance charts like ROC, Lift Curve, Confusion Matrix and others.Look at learning curves to estimate overfitting.Look at model predictions on best/worst cases.More items• | |
C6033 | Simply put, a random sample is a subset of individuals randomly selected by researchers to represent an entire group as a whole. The goal is to get a sample of people that is representative of the larger population. | |
C6034 | If you want a representative sample of a particular population, you need to ensure that:The sample source includes all the target population.The selected data collection method (online, phone, paper, in person) can reach individuals that represent that target population.More items• | |
C6035 | Recall and True Positive Rate (TPR) are exactly the same. So the difference is in the precision and the false positive rate. While precision measures the probability of a sample classified as positive to actually be positive, the false positive rate measures the ratio of false positives within the negative samples. | |
C6036 | Computers analyze, understand and derive meaning by processing human languages using NLP. By analysing text, computers infer how humans speak, and this computerized understanding of human languages can be exploited for numerous use-cases. | |
C6037 | Existing multi-task learning explores the relatedness with other tasks, but disre- gards the consistency among different views of a single task; whereas existing multi-view learning ignores the label information from other related tasks. | |
C6038 | H is the measurement matrix. This matrix influences the Kalman Gain. R is the sensor noise matrix. This matrix implies the measurement error covariance, based on the amount of sensor noise. In this simulation, Q and R are constants, but some implementations of the Kalman Filter may adjust them throughout execution. | |
C6039 | Boosting. Another general machine learning ensemble method is known as boosting. Boosting differs somewhat from bagging as it does not involve bootstrap sampling. | |
C6040 | Genetic algorithms are important in machine learning for three reasons. First, they act on discrete spaces, where gradient-based methods cannot be used. They can be used to search rule sets, neural network architectures, cellular automata computers, and so forth. | |
C6041 | Kmeans clustering algorithm is applied to reduced datasets which is done by principal component analysis dimension reduction method. Cluster analysis is one of the major data analysis methods widely used for many practical applications in emerging areas[12]. | |
C6042 | It is used in multinomial logistic regression and is often used as the last activation function of a neural network to normalize the output of a network to a probability distribution over predicted output classes. , and the components will add up to 1, so that they can be interpreted as probabilities. | |
C6043 | In probability theory, a log-normal (or lognormal) distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed. A log-normal process is the statistical realization of the multiplicative product of many independent random variables, each of which is positive. | |
C6044 | Transfer learning is a machine learning technique where a model trained on one task is re-purposed on a second related task. Transfer learning is an optimization that allows rapid progress or improved performance when modeling the second task. | |
C6045 | Blob detectors can detect areas in an image which are too smooth to be detected by a corner detector. Consider shrinking an image and then performing corner detection. The detector will respond to points which are sharp in the shrunk image, but may be smooth in the original image. | |
C6046 | The standard error is a statistical term that measures the accuracy with which a sample distribution represents a population by using standard deviation. In statistics, a sample mean deviates from the actual mean of a population—this deviation is the standard error of the mean. | |
C6047 | There are several different common loss functions to choose from: the cross-entropy loss, the mean-squared error, the huber loss, and the hinge loss – just to name a few.” | |
C6048 | Data preprocessing in Machine Learning refers to the technique of preparing (cleaning and organizing) the raw data to make it suitable for a building and training Machine Learning models. | |
C6049 | Parametric tests assume underlying statistical distributions in the data. For example, Student's t-test for two independent samples is reliable only if each sample follows a normal distribution and if sample variances are homogeneous. Nonparametric tests do not rely on any distribution. | |
C6050 | E(Y | Xi) = f (Xi) is known as conditional expectation function(CEF) or population regression function (PRF) or population regression (PR) for short. In simple terms, it tells how the mean or average of response of Y varies with X. | |
C6051 | Covariance can be positive, zero, or negative. If X and Y are independent variables, then their covariance is 0: Cov(X, Y ) = E(XY ) − µXµY = E(X)E(Y ) − µXµY = 0 The converse, however, is not always true. | |
C6052 | Once you find a correlation, you can test for causation by running experiments that “control the other variables and measure the difference.” Two such experiments or analyses you can use to identify causation with your product are: Hypothesis testing. A/B/n experiments. | |
C6053 | The population standard deviation is a parameter, which is a fixed value calculated from every individual in the population. A sample standard deviation is a statistic. This means that it is calculated from only some of the individuals in a population. | |
C6054 | Fei-Fei Li, computer vision is defined as “a subset of mainstream artificial intelligence that deals with the science of making computers or machines visually enabled, i.e., they can analyze and understand an image.” Human vision starts at the biological camera's “eyes,” which takes one picture about every 200 | |
C6055 | Fit the regression model by unweighted least squares and analyze the residuals. Estimate the variance function or the standard deviation function. Use the fitted values from the estimated variance or standard deviation function to obtain the weights. Estimate the regression coefficients using these weights. | |
C6056 | Prior probability, in Bayesian statistical inference, is the probability of an event before new data is collected. This is the best rational assessment of the probability of an outcome based on the current knowledge before an experiment is performed. | |
C6057 | Area Under Curve(AUC) is one of the most widely used metrics for evaluation. It is used for binary classification problem. AUC of a classifier is equal to the probability that the classifier will rank a randomly chosen positive example higher than a randomly chosen negative example. | |
C6058 | ) because the integral controller also reduces the rise time and increases the overshoot as the proportional controller does (double effect). The above response shows that the integral controller eliminated the steady-state error in this case. | |
C6059 | The CAC ratio is calculated by looking at the quarter over quarter increase in gross margin divided by the total sales and marketing expenses for that quarter. Gross margin is the total revenue minus cost of goods sold. | |
C6060 | Advantages of Neural Networks:Neural Networks have the ability to learn by themselves and produce the output that is not limited to the input provided to them.The input is stored in its own networks instead of a database, hence the loss of data does not affect its working.More items• | |
C6061 | In computational linguistics, second-order co-occurrence pointwise mutual information is a semantic similarity measure. To assess the degree of association between two given words, it uses pointwise mutual information (PMI) to sort lists of important neighbor words of the two target words from a large corpus. | |
C6062 | There are NO assumptions in any linear model about the distribution of the independent variables. Yes, you only get meaningful parameter estimates from nominal (unordered categories) or numerical (continuous or discrete) independent variables. They do not need to be normally distributed or continuous. | |
C6063 | Because OLS regression is based on the equation for a straight line: y=a+bx. If you have data you know are non-linear, and you attemp to use OLS to regress it, you're guaranteed a large amount of error (which is essentially an error in choosing the model, rather than the data's conformity to it. | |
C6064 | Intuitively, two random variables X and Y are independent if knowing the value of one of them does not change the probabilities for the other one. In other words, if X and Y are independent, we can write P(Y=y|X=x)=P(Y=y), for all x,y. | |
C6065 | Applications of Principal Component Analysis PCA is predominantly used as a dimensionality reduction technique in domains like facial recognition, computer vision and image compression. It is also used for finding patterns in data of high dimension in the field of finance, data mining, bioinformatics, psychology, etc. | |
C6066 | A quantile is the value below which a fraction of observations in a group falls. For example, a prediction for quantile 0.9 should over-predict 90% of the times. Given a prediction yi^p and outcome yi, the mean regression loss for a quantile q is. For a set of predictions, the loss will be its average. | |
C6067 | These are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. | |
C6068 | A canonical variate is a new variable (variate) formed by making a linear combination of two or more variates (variables) from a data set. A linear combination of variables is the same as a weighted sum of variables. | |
C6069 | Simulated Annealing (SA) is a global optimization algorithm. It belongs to the stochastic optimization algorithms. By analogy with this physical process, each step of the SA algorithm attempts to replace the current solution by a random solution till the desired output is obtained. | |
C6070 | Disadvantages of decision trees: They are unstable, meaning that a small change in the data can lead to a large change in the structure of the optimal decision tree. They are often relatively inaccurate. Many other predictors perform better with similar data. | |
C6071 | A latent variable is a random variable which you can't observe neither in training nor in test phase . It is derived from the latin word latēre which means hidden. Intuitionally, some phenomenons like incidences,altruism one can't measure while others like speed or height one can. | |
C6072 | Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes. | |
C6073 | 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. | |
C6074 | In mathematics, statistics, finance, computer science, particularly in machine learning and inverse problems, regularization is the process of adding information in order to solve an ill-posed problem or to prevent overfitting. | |
C6075 | The fitness function simply defined is a function which takes a candidate solution to the problem as input and produces as output how “fit” our how “good” the solution is with respect to the problem in consideration. Calculation of fitness value is done repeatedly in a GA and therefore it should be sufficiently fast. | |
C6076 | The cross-entropy compares the model's prediction with the label which is the true probability distribution. The cross-entropy goes down as the prediction gets more and more accurate. It becomes zero if the prediction is perfect. As such, the cross-entropy can be a loss function to train a classification model. | |
C6077 | Sequence-to-sequence learning (Seq2Seq) is about training models to convert sequences from one domain (e.g. sentences in English) to sequences in another domain (e.g. the same sentences translated to French). | |
C6078 | The squared error has some nice properties: It is symmetrical. That means, if the actual value is and you predict or , you get the same error measure. | |
C6079 | Currently AI is Used is Following Things/Fields: Retail, Shopping and Fashion. Security and Surveillance. Sports Analytics and Activities. Manufacturing and Production. | |
C6080 | Di Excel, klik Power Pivot > Kelola untuk membuka jendela Power Pivot . Menampilkan tab di jendela Power Pivot . Setiap tab berisi tabel dalam model Anda. Kolom dalam setiap tabel muncul sebagai bidang dalam daftar bidang PivotTable. | |
C6081 | In statistical hypothesis testing, a type I error is the rejection of a true null hypothesis (also known as a "false positive" finding or conclusion; example: "an innocent person is convicted"), while a type II error is the non-rejection of a false null hypothesis (also known as a "false negative" finding or conclusion | |
C6082 | The general guideline is to use linear regression first to determine whether it can fit the particular type of curve in your data. If you can't obtain an adequate fit using linear regression, that's when you might need to choose nonlinear regression. | |
C6083 | Homogeneous sampling is a purposive sampling technique that aims to achieve a homogeneous sample; that is, a sample whose units (e.g., people, cases, etc.) share the same (or very similar) characteristics or traits (e.g., a group of people that are similar in terms of age, gender, background, occupation, etc.). | |
C6084 | The cumulative frequency is calculated by adding each frequency from a frequency distribution table to the sum of its predecessors. The last value will always be equal to the total for all observations, since all frequencies will already have been added to the previous total. | |
C6085 | The three main metrics used to evaluate a classification model are accuracy, precision, and recall. Accuracy is defined as the percentage of correct predictions for the test data. It can be calculated easily by dividing the number of correct predictions by the number of total predictions. | |
C6086 | A/B testing is one of the components of the overarching process of Conversion Rate Optimization (CRO) using which you can gather both qualitative and quantitative user insights and use them to understand your potential customers and to optimize your conversion funnel based on that data. | |
C6087 | Sample moments are those that are utilized to approximate the unknown population moments. Sample moments are calculated from the sample data. Such moments include mean, variance, skewness, and kurtosis. | |
C6088 | The hclust function in R uses the complete linkage method for hierarchical clustering by default. This particular clustering method defines the cluster distance between two clusters to be the maximum distance between their individual components. | |
C6089 | Yes, although 'linear regression' refers to any approach to model the relationship between one or more variables, OLS is the method used to find the simple linear regression of a set of data. | |
C6090 | Java, Python, Lisp, Prolog, and C++ are major AI programming language used for artificial intelligence capable of satisfying different needs in the development and designing of different software. | |
C6091 | Answer. To calculate the class interval, first step is to rewrite the table by including the values of mid-interval in place of the values given in range. Then the sum of all the mid- interval values is calculated. | |
C6092 | Classification is a type of supervised learning. It specifies the class to which data elements belong to and is best used when the output has finite and discrete values. It predicts a class for an input variable as well. | |
C6093 | Definition: Bagging is used when the goal is to reduce the variance of a decision tree classifier. Here the objective is to create several subsets of data from training sample chosen randomly with replacement. Each collection of subset data is used to train their decision trees. | |
C6094 | AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. The process requires multiple passes at the data to find connections and derive meaning from undefined data. | |
C6095 | Trajectory clustering aims at finding out trajectories that are of the same (or similar) pattern, or distinguishing some undesired behaviors (such as outliers). The activities of moving objects are often recorded as their trajectories. | |
C6096 | If skewness is positive, the data are positively skewed or skewed right, meaning that the right tail of the distribution is longer than the left. If skewness is negative, the data are negatively skewed or skewed left, meaning that the left tail is longer. If skewness = 0, the data are perfectly symmetrical. | |
C6097 | A squashing function is essentially defined as a function that squashes the input to one of the ends of a small interval. In Neural Networks, these can be used at nodes in a hidden layer to squash the input. Popular ones that have been used include the sigmoid function, hyperbolic tangent function, etc. | |
C6098 | You can use regression equations to make predictions. Regression equations are a crucial part of the statistical output after you fit a model. However, you can also enter values for the independent variables into the equation to predict the mean value of the dependent variable. | |
C6099 | You can use a bivariate Pearson Correlation to test whether there is a statistically significant linear relationship between height and weight, and to determine the strength and direction of the association. |
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