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We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization?
1. We do feature normalization so that new feature will dominate other
2. Some times, feature normalization is not feasible in case of categorical variables
3. Feature normalization always helps when we use Gaussian kernel in SVM
Public key encryption uses multiple keys.ne key is used to encrypt data, while another is used to decrypt data.he key used to encrypt data is called the _______ key, while the key used to decrypt data is called the ________ key.
Given the following statements: S1: A foreign key declaration can always be replaced by an equivalent check assertion in SQL. S2: Given the table R (a, b, c) where a and b together form the primary key, the following is a valid table definition. CREATE TABLE S (                 a INTEGER,                 d INTEGER,                 e INTEGER,                 PRIMARY KEY (d)                 FOREIGN KEY (a) references R) Which one of the following statements is CORRECT?
A pulley is connected to a power transmission shaft of diameter d by means of a rectangular sunk key of width w and length l. The width of the key is taken as d/4. For full power transmission, the shearing strength of the key is equal to the torsional shearing strength of the shaft. The ratio of the length of the key to the diameter of the shaft (l/d) is
A private key and its mathematically related public key, which are so related that the public key, can verify a digital signature created by the private key, in an Asymmetric Crypto system means:
'Existentialism' কি?
In which among the following work, Sartre tried to reconcile Existentialism and Marxism?
Which of the following statements are true according to existentialism and Gita?
Who is widely known as the first existentialist, despite never having used the word existentialism?
Which of the following statements is not true about a splitting rule at internal nodes of the tree based on thresholding the value of a single feature? , where i ∈ is the index of the relevant feature b) It move to the right or left child of the node on the basis of 1, where ϑ ∈ R is the threshold c) Here a decision tree splits the instance space, X = Rd, into cells, where each leaf of the tree corresponds to one cell d) Splits based on thresholding the value of a single feature are also known as multivariate splits