<p style="box-sizing: border-box; margin: 0px 0px 20px; color: #272930; font-family: Roboto, Arial, sans-serif; font-size: 15px;">Let&rsquo;s look at the components <span style="box-sizing: border-box;">and</span> steps to understand Naive Bayes.

<p style="box-sizing: border-box; margin: 0px 0px 20px; color: #272930; font-family: Roboto, Arial, sans-serif; font-size: 15px;">Bayes Theorem The Bayes Theorem is used to calculate conditional probability. The <span style="box-sizing: border-box;">for</span>mula below can be used to determine the likelihood of an event occurring if event B has already occurred. DATA Science Course in Pune<br style="box-sizing: border-box;" /><br style="box-sizing: border-box;" />P(A|B) = (P(B|A) * P(A)) / P(B)

<p style="box-sizing: border-box; margin: 0px 0px 20px; color: #272930; font-family: Roboto, Arial, sans-serif; font-size: 15px;">Data Sciences Course in Pune. Data Sciences Courses in Pune

<p style="box-sizing: border-box; margin: 0px 0px 20px; color: #272930; font-family: Roboto, Arial, sans-serif; font-size: 15px;">During the initial <span style="box-sizing: border-box;">training</span> phase, an algorithm is created using the labeled data. Assume we have a dataset that has N instances and M attributes. The class label represents a target variable.

<p style="box-sizing: border-box; margin: 0px 0px 20px; color: #272930; font-family: Roboto, Arial, sans-serif; font-size: 15px;">Prior probabilities (P(C),), The algorithm calculates P(C), which is the prior probability of a random instance that belongs to a specific class.

<p style="box-sizing: border-box; margin: 0px 0px 20px; color: #272930; font-family: Roboto, Arial, sans-serif; font-size: 15px;">(C) each feature Fi with a class label. This is the probability of a particular feature being present if an instance belongs to a specific class.

<p style="box-sizing: border-box; margin: 0px 0px 20px; color: #272930; font-family: Roboto, Arial, sans-serif; font-size: 15px;">Data Science Courses in Pune

<p style="box-sizing: border-box; margin: 0px 0px 20px; color: #272930; font-family: Roboto, Arial, sans-serif; font-size: 15px;">Bayes&rsquo; theorem is used to determine class of unknown instances.

<p style="box-sizing: border-box; margin: 0px 0px 20px; color: #272930; font-family: Roboto, Arial, sans-serif; font-size: 15px;">P(C|X) = (P(X|C) * P(C)) / P(X)

<p style="box-sizing: border-box; margin: 0px 0px 20px; color: #272930; font-family: Roboto, Arial, sans-serif; font-size: 15px;">Multiplication of the probability (P(xi), by the prior probability (P(C)).

<p style="box-sizing: border-box; margin: 0px 0px 20px; color: #272930; font-family: Roboto, Arial, sans-serif; font-size: 15px;">Predicting Class: The algorithm chooses the class label that has the highest posterior probabilities based on a given example.

<p style="box-sizing: border-box; margin: 0px 0px 20px; color: #272930; font-family: Roboto, Arial, sans-serif; font-size: 15px;">Data Science Training in Pune- Laplace smoothing is used when there <span style="box-sizing: border-box;">are</span> no feature values in the class training values. Data Science Training in Pune &ndash; This is achieved by adding a small variable.

<p style="box-sizing: border-box; margin: 0px 0px 20px; color: #272930; font-family: Roboto, Arial, sans-serif; font-size: 15px;">Naive Bayes is suitable for binary and multiclass classification problems. It is especially well-suited to text classification tasks such as spam detection or emotion analysis where independence assumptions are reasonable.

<p style="box-sizing: border-box; margin: 0px 0px 10px; color: #272930; font-family: Roboto, Arial, sans-serif; font-size: 15px;">Naive Bayes, in conclusion, is a probabilistic classification algorithm that relies on Bayes&rsquo;s theorem as well as an assumption of feature-independence. It has been widely used despite its simplicity because of its effectiveness and efficiency in real-world applications.

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