Let's look at the components <span class="t_tag" style="overflow-wrap: break-word; line-height: normal; cursor: pointer; border-bottom: 1px solid #ff0000; text-wrap: nowrap; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;">and</span> steps to understand Naive Bayes.<br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" /><br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" />Bayes Theorem The Bayes Theorem is used to calculate conditional probability. The <span class="t_tag" style="overflow-wrap: break-word; line-height: normal; cursor: pointer; border-bottom: 1px solid #ff0000; text-wrap: nowrap; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;">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="overflow-wrap: break-word; line-height: normal;" /><br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" />P(A|B) = (P(B|A) * P(A)) / P(B)<br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" /><br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" />Data Sciences Course in Pune. Data Sciences Courses in Pune<br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" /><br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" />During the initial <span class="t_tag" style="overflow-wrap: break-word; line-height: normal; cursor: pointer; border-bottom: 1px solid #ff0000; text-wrap: nowrap; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;">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.<br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" /><br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" />Prior probabilities (P(C),), The algorithm calculates P(C), which is the prior probability of a random instance that belongs to a specific class.<br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" /><br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" />(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.<br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" /><br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" />Data Science Courses in Pune<br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" /><br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" />Bayes' theorem is used to determine class of unknown instances.<br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" /><br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" />P(C|X) = (P(X|C) * P(C)) / P(X)<br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" /><br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" />Multiplication of the probability (P(xi), by the prior probability (P(C)).<br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" /><br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" />Predicting Class: The algorithm chooses the class label that has the highest posterior probabilities based on a given example.<br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" /><br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" />Data Science Training in Pune- Laplace smoothing is used when there <span class="t_tag" style="overflow-wrap: break-word; line-height: normal; cursor: pointer; border-bottom: 1px solid #ff0000; text-wrap: nowrap; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;">are</span> no feature values in the class training values. Data Science Training in Pune - This is achieved by adding a small variable.<br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" /><br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" />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.<br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" /><br style="overflow-wrap: break-word; line-height: normal; color: #444444; font-family: Verdana, Helvetica, Arial, sans-serif;" />Naive Bayes, in conclusion, is a probabilistic classification algorithm that relies on Bayes'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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