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C4100 | Correlation is a technique for investigating the relationship between two quantitative, continuous variables, for example, age and blood pressure. Pearson's correlation coefficient (r) is a measure of the strength of the association between the two variables. | |
C4101 | They are sometimes called "normal" values. By comparing your test results with reference values, you and your healthcare provider can see if any of your test results fall outside the range of expected values. Values that are outside expected ranges can provide clues to help identify possible conditions or diseases. | |
C4102 | Collective Intelligence. knowledge collected from many people towards a common goal. | |
C4103 | The Central Limit Theorem and Means In other words, add up the means from all of your samples, find the average and that average will be your actual population mean. Similarly, if you find the average of all of the standard deviations in your sample, you'll find the actual standard deviation for your population. | |
C4104 | Construct validity means the test measures the skills/abilities that should be measured. Content validity means the test measures appropriate content. | |
C4105 | The distribution for z is the standard normal distribution; it has a mean of 0 and a standard deviation of 1. For Ha: p ≠ 26, the P-value would be P(z ≤ -1.83) + P(z ≥ 1.83) = 2 * P(z ≤ -1.83). Regardless of Ha, z = (p̂ - p0) / sqrt(p0 * (1 - p0) / n), where z gives the number of standard deviations p̂ is from p0. | |
C4106 | The input gate controls the extent to which a new value flows into the cell, the forget gate controls the extent to which a value remains in the cell and the output gate controls the extent to which the value in the cell is used to compute the output activation of the LSTM unit. | |
C4107 | The difference is a matter of design. In the test of independence, observational units are collected at random from a population and two categorical variables are observed for each unit. In the goodness-of-fit test there is only one observed variable. | |
C4108 | 1. Which search agent operates by interleaving computation and action? Explanation: In online search, it will first take an action and then observes the environment. | |
C4109 | Sensitivity aka Recall (true positives / all actual positives) = TP / TP + FN. 45 / (45 + 5) = 45 / 50 = 0.90 or 90% Sensitivity. 5. Specificity (true negatives / all actual negatives) =TN / TN + FP. | |
C4110 | Theoretically, yes. TensorFlow is designed with flexibility in mind, so that should be possible. | |
C4111 | A negative correlation can indicate a strong relationship or a weak relationship. Many people think that a correlation of –1 indicates no relationship. But the opposite is true. A correlation of -1 indicates a near perfect relationship along a straight line, which is the strongest relationship possible. | |
C4112 | The Kappa Architecture was first described by Jay Kreps. It focuses on only processing data as a stream. It is not a replacement for the Lambda Architecture, except for where your use case fits. The idea is to handle both real-time data processing and continuous reprocessing in a single stream processing engine. | |
C4113 | Empirical Relationship between Mean, Median and Mode In case of a moderately skewed distribution, the difference between mean and mode is almost equal to three times the difference between the mean and median. Thus, the empirical mean median mode relation is given as: Mean – Mode = 3 (Mean – Median) | |
C4114 | Statistical knowledge helps you use the proper methods to collect the data, employ the correct analyses, and effectively present the results. Statistics is a crucial process behind how we make discoveries in science, make decisions based on data, and make predictions. | |
C4115 | The 7 Steps of Machine Learning1 - Data Collection. The quantity & quality of your data dictate how accurate our model is. 2 - Data Preparation. Wrangle data and prepare it for training. 3 - Choose a Model. 4 - Train the Model. 5 - Evaluate the Model. 6 - Parameter Tuning. 7 - Make Predictions. | |
C4116 | A feature selection method is proposed to select a subset of variables in principal component analysis (PCA) that preserves as much information present in the complete data as possible. The information is measured by means of the percentage of consensus in generalised Procrustes analysis. | |
C4117 | Active learning engages students in learning, using activities such as reading, writing, discussion, or problem solving, which promote analysis, synthesis, and evaluation of class content. Active in-class learning also provides students with informal opportunities for feedback on how well they understood the material. | |
C4118 | Structural equation modeling (SEM) is a multivariate statistical framework that is used to model complex relationships between directly and indirectly observed (latent) variables. Modeling the aggregate effects of common and rare variants in multiple potentially interesting genes using latent variable SEM. | |
C4119 | slang. a dismissal; discharge. They gave him the boot for coming in late. 17. informal. | |
C4120 | A random effect model is a model all of whose factors represent random effects. (See Random Effects.) Such models are also called variance component models. Random effect models are often hierarchical models. A model that contains both fixed and random effects is called a mixed model. | |
C4121 | Stratified random sampling is used when your population is divided into strata (characteristics like male and female or education level), and you want to include the stratum when taking your sample. | |
C4122 | Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. To find a local minimum of a function using gradient descent, we take steps proportional to the negative of the gradient (or approximate gradient) of the function at the current point. | |
C4123 | No. Principal components analysis involves breaking down the variance structure of a group of variables. Categorical variables are not numerical at all, and thus have no variance structure. | |
C4124 | The general procedure for using regression to make good predictions is the following:Research the subject-area so you can build on the work of others. Collect data for the relevant variables.Specify and assess your regression model.If you have a model that adequately fits the data, use it to make predictions. | |
C4125 | Important Properties Property #1: The total area under a t distribution curve is 1.0: that is 100%. Property #2: A t-curve is symmetric around 0. Property #3: While a t-curve extends infinitely in either direction, it approaches, but never touches the horizontal axis. | |
C4126 | 1a : to divide into parts or shares. b : to divide (a place, such as a country) into two or more territorial units having separate political status. 2 : to separate or divide by a partition (such as a wall) —often used with off. Other Words from partition Synonyms More Example Sentences Learn More about partition. | |
C4127 | In convolutional layers the weights are represented as the multiplicative factor of the filters. For example, if we have the input 2D matrix in green. with the convolution filter. Each matrix element in the convolution filter is the weights that are being trained. | |
C4128 | That's your sample size--the number of participants needed to achieve valid conclusions or statistical significance in quantitative research. When sample sizes are too small, you run the risk of not gathering enough data to support your hypotheses or expectations. | |
C4129 | Neural Networks and Deep Reinforcement Learning. Neural networks are function approximators, which are particularly useful in reinforcement learning when the state space or action space are too large to be completely known. A neural network can be used to approximate a value function, or a policy function. | |
C4130 | k-Means Clustering is an unsupervised learning algorithm that is used for clustering whereas KNN is a supervised learning algorithm used for classification. | |
C4131 | Cross Entropy is definitely a good loss function for Classification Problems, because it minimizes the distance between two probability distributions - predicted and actual. So cross entropy make sure we are minimizing the difference between the two probability. This is the reason. | |
C4132 | A covariance matrix is a square matrix which gives two types of information. If you are looking at the population covariance matrix then. each diagonal element is the variance of the corresponding random variable. each off-diagonal element is the covariance of the corresponding pair of random variables. | |
C4133 | In the statistical theory of design of experiments, randomization involves randomly allocating the experimental units across the treatment groups. Randomization reduces bias by equalising other factors that have not been explicitly accounted for in the experimental design (according to the law of large numbers). | |
C4134 | In computational mathematics, an iterative method is a mathematical procedure that uses an initial value to generate a sequence of improving approximate solutions for a class of problems, in which the n-th approximation is derived from the previous ones. | |
C4135 | Deep learning is an artificial intelligence (AI) function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Also known as deep neural learning or deep neural network. | |
C4136 | See the following examples from SciPol:Making driving safer. Though self-driving cars are still a few years away from being fully safe to drive, this area of AI could dramatically decrease the rates of deaths and injuries on the roads. Transforming how we learn. Help us become more energy efficient. Helping wildlife. | |
C4137 | Q-learning is a model-free reinforcement learning algorithm to learn quality of actions telling an agent what action to take under what circumstances. "Q" names the function that the algorithm computes with the maximum expected rewards for an action taken in a given state. | |
C4138 | Lemmatization and stemming are the techniques of keyword normalization, while Levenshtein and Soundex are techniques of string matching. | |
C4139 | How to reduce False Positive and False Negative in binary classificationfirstly random forest overfits if the training data and testing data are not drawn from same distribution.check the data for linearity,multicollinearity ,outliers,etc.More items | |
C4140 | Systematic random samplingCalculate the sampling interval (the number of households in the population divided by the number of households needed for the sample)Select a random start between 1 and sampling interval.Repeatedly add sampling interval to select subsequent households. | |
C4141 | Deep learning is a subfield of machine learning, and neural networks make up the backbone of deep learning algorithms. In fact, it is the number of node layers, or depth, of neural networks that distinguishes a single neural network from a deep learning algorithm, which must have more than three. | |
C4142 | A false positive means that the results say you have the condition you were tested for, but you really don't. With a false negative, the results say you don't have a condition, but you really do. | |
C4143 | Statistical classification helps in determining the set to which a particular observation belongs. Multiple methods can be used for the classification process, namely, Frequentest procedure and Bayesian procedure among others. It helps in quicker arranging and collection of data,as well as more efficient work rate. | |
C4144 | Cosine similarity is a metric used to measure how similar the documents are irrespective of their size. The cosine similarity is advantageous because even if the two similar documents are far apart by the Euclidean distance (due to the size of the document), chances are they may still be oriented closer together. | |
C4145 | Use of AI in Following Things/Fields/Areas:Virtual Assistant or Chatbots.Agriculture and Farming.Autonomous Flying.Retail, Shopping and Fashion.Security and Surveillance.Sports Analytics and Activities.Manufacturing and Production.Live Stock and Inventory Management.More items• | |
C4146 | The most basic approach is to stick to the default value and hope for the best. A better implementation of the first option is to test a broad range of possible values. Depending on how the loss changes, you go for a higher or lower learning rate. The aim is to find the fastest rate that still decreases the loss. | |
C4147 | An ordinal variable is a categorical variable for which the possible values are ordered. Ordinal variables can be considered “in between” categorical and quantitative variables. Thus it does not make sense to take a mean of the values. | |
C4148 | Increase Training Dataset Size Leaning on the law of large numbers, perhaps the simplest approach to reduce the model variance is to fit the model on more training data. In those cases where more data is not readily available, perhaps data augmentation methods can be used instead. | |
C4149 | To run the t-test, arrange your data in columns as seen below. Click on the “Data” menu, and then choose the “Data Analysis” tab. You will now see a window listing the various statistical tests that Excel can perform. Scroll down to find the t-test option and click “OK”. | |
C4150 | Deep Q-Networks In deep Q-learning, we use a neural network to approximate the Q-value function. The state is given as the input and the Q-value of all possible actions is generated as the output. | |
C4151 | The performance of deep learning neural networks often improves with the amount of data available. Data augmentation is a technique to artificially create new training data from existing training data. This means, variations of the training set images that are likely to be seen by the model. | |
C4152 | Approximately Normal Distributions with Discrete Data. If a random variable is actually discrete, but is being approximated by a continuous distribution, a continuity correction is needed. | |
C4153 | Noisy data are data with a large amount of additional meaningless information in it called noise. This includes data corruption and the term is often used as a synonym for corrupt data. It also includes any data that a user system cannot understand and interpret correctly. | |
C4154 | Most home pregnancy tests are reliable, for example Clearblue's tests have an accuracy of over 99% from the day you expect your period, and while it's possible a test showing a negative result is wrong, particularly if you're testing early, getting a false positive is extremely rare. | |
C4155 | 8 Methods to Boost the Accuracy of a ModelAdd more data. Having more data is always a good idea. Treat missing and Outlier values. Feature Engineering. Feature Selection. Multiple algorithms. Algorithm Tuning. Ensemble methods. | |
C4156 | Standard deviation is a number used to tell how measurements for a group are spread out from the average (mean or expected value). A low standard deviation means that most of the numbers are close to the average, while a high standard deviation means that the numbers are more spread out. | |
C4157 | A heuristic is admissible if it never overestimates the true cost to a nearest goal. A heuristic is consistent if, when going from neighboring nodes a to b, the heuristic difference/step cost never overestimates the actual step cost. This can also be re-expressed as the triangle inequality men- tioned in Lecture 3. | |
C4158 | A discrete random variable has a countable number of possible values. The probability of each value of a discrete random variable is between 0 and 1, and the sum of all the probabilities is equal to 1. A continuous random variable takes on all the values in some interval of numbers. | |
C4159 | Smoothing techniques in NLP are used to address scenarios related to determining probability / likelihood estimate of a sequence of words (say, a sentence) occuring together when one or more words individually (unigram) or N-grams such as bigram(wi/wi−1) or trigram (wi/wi−1wi−2) in the given set have never occured in | |
C4160 | Accuracy reflects how close a measurement is to a known or accepted value, while precision reflects how reproducible measurements are, even if they are far from the accepted value. Measurements that are both precise and accurate are repeatable and very close to true values. | |
C4161 | A neural network (NN), in the case of artificial neurons called artificial neural network (ANN) or simulated neural network (SNN), is an interconnected group of natural or artificial neurons that uses a mathematical or computational model for information processing based on a connectionistic approach to computation. | |
C4162 | Cluster analysis is a tool for classifying objects into groups and is not concerned with the geometric representation of the objects in a low-dimensional space. To explore the dimensionality of the space, one may use multidimensional scaling. | |
C4163 | Maximum pooling, or max pooling, is a pooling operation that calculates the maximum, or largest, value in each patch of each feature map. The results are down sampled or pooled feature maps that highlight the most present feature in the patch, not the average presence of the feature in the case of average pooling. | |
C4164 | BFS is slower than DFS. DFS is faster than BFS. Time Complexity of BFS = O(V+E) where V is vertices and E is edges. Time Complexity of DFS is also O(V+E) where V is vertices and E is edges. | |
C4165 | 8 Methods to Boost the Accuracy of a ModelAdd more data. Having more data is always a good idea. Treat missing and Outlier values. Feature Engineering. Feature Selection. Multiple algorithms. Algorithm Tuning. Ensemble methods. | |
C4166 | Multicollinearity might be a handful to pronounce but it's a topic you should be aware of in the machine learning field. Due to multicollinearity, regression coefficients will not be estimated precisely and cause a high standard error. | |
C4167 | Selective unsupervised feature learning with Convolutional Neural Network (S-CNN) Abstract: Supervised learning of convolutional neural networks (CNNs) can require very large amounts of labeled data. This method for unsupervised feature learning is then successfully applied to a challenging object recognition task. | |
C4168 | The following methods for validation will be demonstrated:Train/test split.k-Fold Cross-Validation.Leave-one-out Cross-Validation.Leave-one-group-out Cross-Validation.Nested Cross-Validation.Time-series Cross-Validation.Wilcoxon signed-rank test.McNemar's test.More items | |
C4169 | If the P-value is less than (or equal to) , then the null hypothesis is rejected in favor of the alternative hypothesis. And, if the P-value is greater than , then the null hypothesis is not rejected. | |
C4170 | Posterior probability = prior probability + new evidence (called likelihood). For example, historical data suggests that around 60% of students who start college will graduate within 6 years. This is the prior probability. However, you think that figure is actually much lower, so set out to collect new data. | |
C4171 | The lognormal distribution is a distribution skewed to the right. The pdf starts at zero, increases to its mode, and decreases thereafter. The degree of skewness increases as increases, for a given . For the same , the pdf's skewness increases as increases. | |
C4172 | The key classification metrics: Accuracy, Recall, Precision, and F1- Score. | |
C4173 | On-policy methods attempt to evaluate or improve the policy that is used to make decisions. In contrast, off-policy methods evaluate or improve a policy different from that used to generate the data. | |
C4174 | How to choose the size of the convolution filter or Kernel size1x1 kernel size is only used for dimensionality reduction that aims to reduce the number of channels. It captures the interaction of input channels in just one pixel of feature map. 2x2 and 4x4 are generally not preferred because odd-sized filters symmetrically divide the previous layer pixels around the output pixel. | |
C4175 | 4.7 Confusion matrix patterns The “normalized” term means that each of these groupings is represented as having 1.00 samples. The columns sum the samples assigned to each class, and the diagonal elements divided by these sums are the precision values. The diagonal elements represent the recall values. | |
C4176 | High Dimensional means that the number of dimensions are staggeringly high — so high that calculations become extremely difficult. With high dimensional data, the number of features can exceed the number of observations. For example, microarrays, which measure gene expression, can contain tens of hundreds of samples. | |
C4177 | The ROC curve shows the trade-off between sensitivity (or TPR) and specificity (1 – FPR). Classifiers that give curves closer to the top-left corner indicate a better performance. As a baseline, a random classifier is expected to give points lying along the diagonal (FPR = TPR). | |
C4178 | If you want to process the gradients before applying them you can instead use the optimizer in three steps:Compute the gradients with compute_gradients().Process the gradients as you wish.Apply the processed gradients with apply_gradients(). | |
C4179 | Q-Learning is a value-based reinforcement learning algorithm which is used to find the optimal action-selection policy using a Q function. Our goal is to maximize the value function Q. The Q table helps us to find the best action for each state. | |
C4180 | 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. | |
C4181 | Regression analysis is a reliable method of identifying which variables have impact on a topic of interest. The process of performing a regression allows you to confidently determine which factors matter most, which factors can be ignored, and how these factors influence each other. | |
C4182 | Conditional probability is the probability of one event occurring with some relationship to one or more other events. For example: Event A is that it is raining outside, and it has a 0.3 (30%) chance of raining today. Event B is that you will need to go outside, and that has a probability of 0.5 (50%). | |
C4183 | Univariate linear regression focuses on determining relationship between one independent (explanatory variable) variable and one dependent variable. Regression comes handy mainly in situation where the relationship between two features is not obvious to the naked eye. | |
C4184 | Thus, the SMC counts both mutual presences (when an attribute is present in both sets) and mutual absence (when an attribute is absent in both sets) as matches and compares it to the total number of attributes in the universe, whereas the Jaccard index only counts mutual presence as matches and compares it to the | |
C4185 | While statistical significance relates to whether an effect exists, practical significance refers to the magnitude of the effect. However, no statistical test can tell you whether the effect is large enough to be important in your field of study. An effect of 4 points or less is too small to care about. | |
C4186 | In lay terms, the standard deviation can be thought of as roughly the average distance of scores from the mean. Precisely, the standard deviation is defined to be the square root of the average squared deviation of scores from the mean. | |
C4187 | The Hidden Markov Model (HMM) is a relatively simple way to model sequential data. A hidden Markov model implies that the Markov Model underlying the data is hidden or unknown to you. More specifically, you only know observational data and not information about the states. | |
C4188 | A positive coefficient indicates that as the value of the independent variable increases, the mean of the dependent variable also tends to increase. A negative coefficient suggests that as the independent variable increases, the dependent variable tends to decrease. | |
C4189 | A simple way to fix imbalanced data-sets is simply to balance them, either by oversampling instances of the minority class or undersampling instances of the majority class. This simply allows us to create a balanced data-set that, in theory, should not lead to classifiers biased toward one class or the other. | |
C4190 | The sample space of a random experiment is the collection of all possible outcomes. An event associated with a random experiment is a subset of the sample space. The probability of any outcome is a number between 0 and 1. The probabilities of all the outcomes add up to 1. | |
C4191 | Whereas AI is preprogrammed to carry out a task that a human can but more efficiently, artificial general intelligence (AGI) expects the machine to be just as smart as a human. | |
C4192 | The loss and accuracy of all three models is comparable but the Neocognitron and Coward model have a higher processing time than the Convolutional Neural Network. It is also evident that the Neocognitron requires more training steps than the Convolutional Neural Network to reach the same accuracy and loss. | |
C4193 | Decision tree learning is generally best suited to problems with the following characteristics: Instances are represented by attribute-value pairs. There is a finite list of attributes (e.g. hair colour) and each instance stores a value for that attribute (e.g. blonde). | |
C4194 | Here are the steps for finding any percentile for a normal distribution X: 1a. If you're given the probability (percent) less than x and you need to find x, you translate this as: Find a where p(X < a) = p (and p is the given probability). That is, find the pth percentile for X. | |
C4195 | A hypothesis is an approximate explanation that relates to the set of facts that can be tested by certain further investigations. There are basically two types, namely, null hypothesis and alternative hypothesis. A research generally starts with a problem. | |
C4196 | Definition. The explained sum of squares (ESS) is the sum of the squares of the deviations of the predicted values from the mean value of a response variable, in a standard regression model — for example, yi = a + b1x1i + b2x2i + the value estimated by the regression line . | |
C4197 | Average: Theory & Formulas. We all know that the average is sum of observations divided by the total number of observations. Average Formula = Sum of observations/ Number of observations. This is the simple formula which helps us to calculate the average in math. | |
C4198 | 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.More items | |
C4199 | their superior predictive power and their theoretical foundation. their accuracy is poor in many domains compared to neural networks. |
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